{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "0d4b54fe",
   "metadata": {},
   "source": [
    "## 数据探索与清洗\n",
    "- 检查缺失值并进行处理\n",
    "- 分析各特征的数据类型和分布\n",
    "- 处理异常值和重复数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "dcd8d1e1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 12 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  891 non-null    int64  \n",
      " 1   Survived     891 non-null    int64  \n",
      " 2   Pclass       891 non-null    int64  \n",
      " 3   Name         891 non-null    object \n",
      " 4   Sex          891 non-null    object \n",
      " 5   Age          714 non-null    float64\n",
      " 6   SibSp        891 non-null    int64  \n",
      " 7   Parch        891 non-null    int64  \n",
      " 8   Ticket       891 non-null    object \n",
      " 9   Fare         891 non-null    float64\n",
      " 10  Cabin        204 non-null    object \n",
      " 11  Embarked     889 non-null    object \n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 83.7+ KB\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "ori_df = pd.read_csv('titanic.csv')\n",
    "ori_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "cb78b3b0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          Missing Count  Missing Percentage\n",
      "cabin               687           77.104377\n",
      "age                 177           19.865320\n",
      "embarked              2            0.224467\n"
     ]
    },
    {
     "data": {
      "image/png": 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IZXZLuIgAAGQLQTeA0AhSAQCZKo35REoWxwEAAAAiRqYbQGiUlwAAMlUa70Q3QTeA8AhSAQDIDEE3gNDIdAMAMpWweKOmGwAAAIgYmW4AoZEZBgBkqtSKLM4IugEAQFq4AEcmEjGfSEl5CQAAABAxMt0AACC2k6rJ2mdPaczLS8h0AwAAABEj0w0AAIDIlcY8003QDQAAYluSUUilMshvBN0AACC2gWohXUDI/DxegSYRkOkGgNidaAvxZAsAyF8E3QBCI0gFAGSqlJpuAAAAIFqlMW+aF+9HDwAAAGQBmW4AAABELsFESgAAgPCY3wGkj6AbQGh0LwFQaMcCjgPZU8pESgAIh5MTAACZIegGAABA5EqDePfviPejBwAAALKATDcAAAAil4h5rjfejx4AAADIAjLdAAAgLUyqRiZK6V4CAAAQHi0DkYnSmE+kJOgGAABpIVAF0kfQDQAA0kKmG5lIUF4CAAAQHoEqkD6CbgAAkBYy3chEacyb5sX70QMAAABZQKYbAACkhewwMlFK9xIAAIDwKC8B0kfQDQAAgMglYl7VTNANAACAyJUGtAwEAAAIjZIMIH0E3QAAIC3UdCMTpTEvL4n3owcAAACygEw3AAAAIpeIecvAeD96AAAAIAvIdAMAACBypTHP9RJ0A4jl5ClhAhUAIFsIugGERpAKQDgWIBOl9OkGAACI56gXFxDZk4h5eUm8Hz0AAACQBWS6AQAAELlSWgYCAAAAiBKZbgAAAEQuYfGeSEmmGwAAAIgYmW4AAJAWOn4gE6XUdAMAAACIEpluALHszStk64DMFNKxgONA9pTGPNdL0A0gNE5OAIBMJWK+ImW8LzkAAACALCDTDQAAgMiVxjzXS9ANAADSQqkZkD6CbgAAkBYmUiITCVoGAgAAAIgSmW4AscxuCRkuAMie0pgvA0/QDSA0glQAADJD0A0gNDLdAIBMJWJe003QDSA0glQAQKZKY15eEu9LDgAAACALyHQDAAAgcgnKSwAgHGq6AQjvHSB9BN0AQuNEC6DQLsA5rmVPacwz3fF+9AAAAEAWEHQDAAAgcgkriuyWrgkTJliLFi2sdu3a1qFDB1u8ePFmf37cuHG2xx572FZbbWXNmze3QYMG2c8//5zW3yToBgAAQGzMmjXLBg8ebCNHjrQlS5ZY69atrVu3bvbll19W+fMzZsywoUOHup9///337Z577nH38X/+z/9J6+8SdAMAACArNd2lEd3Sccstt1i/fv2sb9++tvfee9ukSZOsTp06NnXq1Cp//pVXXrFDDjnEzjzzTJcdP/roo61Xr16/mh2viImUAAAgLUw+RCYSQXSL45SUlLhbqlq1arlbqg0bNtibb75pw4YNK9tWXFxsXbp0sUWLFlV53x07drT777/fBdnt27e3jz/+2ObMmWNnn312WvtI0A0AANJC9xLkm7Fjx9ro0aPLbVM5yKhRo8ptW7t2rZWWllqTJk3KbdfXS5curfK+leHW7x166KEWBIFt3LjRLrroorTLSwi6AcTyRCucbAEge0ojrGoeMWyYq9NOVTHLnamFCxfamDFjbOLEiW7S5UcffWQDBw60q6++2v7617+Gvh+CbgChEaQCEI4FyDe1qiglqUqjRo2sWrVqtnr16nLb9XXTpk2r/B0F1iolOf/8893X++23n61bt84uuOACu/LKK115ShgE3QBCI9MNAMjHmu6watasaW3btrUFCxZYjx493LZEIuG+7t+/f5W/s379+kqBtQJ3UblJWATdAEIjSAVQaBfgHNfiZ/DgwdanTx9r166dmxipHtzKXKubifTu3duaNWvm6sTl+OOPdx1P9t9//7LyEmW/tT0ZfIdB0A0AAIDIJfKkU3XPnj1tzZo1NmLECFu1apW1adPG5s6dWza5cuXKleUy28OHD7eioiL38bPPPrPtt9/eBdzXXnttWn+3KAiZF+9afFq6jwkAABQgMt35a35ituWry9/uGdl939R6luU7Mt0AACDWgSqyozQParpziaAbAAAAsZhImUsE3QBiOaQsZOuAzBTSsYDjALKFoBsAAACRSwT5MZEyVwi6AYRGRggAgMwQdAMAACBypRbvmu545/kBAACALCDTDQAAgMglYt69hEw3AAAAEDEy3QAAAIhcgu4lAAAAQLQSMZ9ISdANIJYLYggtEAEA2ULQDSA0glQAQKZKYz6RkqAbAACkhQtwIH0E3QAAILalZlxAZE8i5hMp4/3oAQAAgCwg0w0gltktIcMFANmToKYbAMIhSAUAIDME3QAAAIhcgj7dABAO5SUAAGSGoBtAaASpAIBMJajpBgAAAKKViHnLQIJuAKFRXgIAQGYIugGERpAKAMhUIublJfHO8wMAAABZQKYbQGiUlwAAMpWgZSAAhEOQCgBAZgi6AQBAWrgARyYSMa/pJugGAACxLTXjAgLZQtANAADSQqCKTCTIdANA/LJbQuAAZKaQjgUcB7InQdANAOFwcgIgHAuA9BF0AwCAtJDpRiYSZLoBIH4nWuFkCwDIFoJuAKERpAIAMpVgcRwACIdMNwAAmSHoBhAaQSoA4ViATCSo6QaAcMh0Ayi0YwHHAWQLQTeA0Dg5AQAylSDTDQAAEB4X4MhEgqAbAOI3pCwEDkBmCulYwHEA2ULQDSA0Tk4AgEwlyHQDQPyyW8JFBAAgWwi6AYRGkApAOBYgEwGZbgAIh0w3gEI7FnAcQLYQdAMIjZMTACBTiZgvA1+c6x0AAAAACh2ZbgAAAEQuQU03AAAAEK0g5kE35SUAAABAxMh0AwAAIHKJmGe6CboBAEBa6GQEpI+gGwAApIU+3chEQKYbAOJ3ohVOtgCAbCHoBhAaQSoA4ViATCRinummewkAAAAQMTLdAEKjvARAoR0LOA5kTxBYrBF0AwiNkxMAIFMJi3d5CUE3gFhmt4SLCABAthB0AwAAIHJBzCdSEnQDCI3MMAAAmSHoBgAAaeECHJlIkOkGAACI5/wOLiCQLQTdAAAAiFwQ85aBLI4DAAAARIxMNwAASAslGchEQE03AABAeNR0IxNBzINuyksAAACAiJHpBhDL7JaQ4QKA7EnEPNNN0A0gNIJUAAAyQ9ANAACAyAUxbxlI0A0AANLCqBeQPoJuAAAARC6gphsAACCek6rJ2iNbCLoBxPJEK5xsASB7AjLdABAOQSoA4ViATAQWbyyOAwAAAESMTDcAAAAiF1BeAgAAEM/5HZTKIFsIugEAQFoIVJGRwGKNoBtALLNbQuAAZKaQjgUcB5AtBN0AQuPkBADIVEBNNwDEL7slXEQAmeG9A99NmDDBbrzxRlu1apW1bt3a7rjjDmvfvv0mf/7bb7+1K6+80h555BH7+uuvbeedd7Zx48ZZ9+7dQ/9Ngm4AoXGiBVBoF+Ac17InyJOa7lmzZtngwYNt0qRJ1qFDBxc8d+vWzZYtW2aNGzeu9PMbNmywrl27uu899NBD1qxZM/v000+tQYMGaf1dgm4AAAB4XV5SUlLibqlq1arlbhXdcsst1q9fP+vbt6/7WsH3k08+aVOnTrWhQ4dW+nltV3b7lVdesRo1arhtLVq0SHsfi4Ig3HVH1+LT0r5zAAAAZM/8xGzLV7vOujay+z77/V9s9OjR5baNHDnSRo0aVSlrXadOHZex7tGjR9n2Pn36uBKSxx9/vNJ9q4Rk2223db+n72+//fZ25pln2hVXXGHVqlULvY9kugHEckhZGFYGMlNIxwKOA1kURJfpHjZsmCsZSVVVlnvt2rVWWlpqTZo0KbddXy9durTK+/7444/t2WeftbPOOsvmzJljH330kV1yySX2yy+/uMA+LIJuAKFxcgIA5KNamygl2RISiYSr5548ebLLbLdt29Y+++wzNxGToBsAAAB5JciDiZSNGjVygfPq1avLbdfXTZs2rfJ3dthhB1fLnVpKstdee7nOJypXqVmzZqi/TdANIJZDykLmHgDipWbNmi5TvWDBgrKabmWy9XX//v2r/J1DDjnEZsyY4X6uuLjYbfvggw9cMB424BaCbgChEaQCEI4FyEhgeUG135o42a5dO9ebWy0D161bV9bNpHfv3q4t4NixY93XF198sY0fP94GDhxoAwYMsA8//NDGjBljf/7zn9P6uwTdAEIj0w2g0I4FHAfip2fPnrZmzRobMWKEKxFp06aNzZ07t2xy5cqVK8sy2tK8eXObN2+eDRo0yFq1auUCcgXg6l6SDloGAgAAFIh8bhnY8oH/lzmOwidnDbN8R6YbAACkhUw3kD6CbgAAAMSmpjtXCLoBAADg9TLwPvj/VeIAAAAAIkGmGwAAANELLNbIdAMAAAARI9MNAACALCiyOCPTDQAAAESMTDcAAACiF1iskekGAAAAIkamGwAAANELLNYIugEAQFpYOh0ZCeI9kZKgGwAApGXe529boeACAtlC0A0AAIDIBTEvL2EiJQAAABAxMt0AAACIXmCxRqYbAAAAiBiZbgAAAEQviHf3EjLdAAAAQMTIdAOIZZswoVUYAGRPUcxrugm6AQAAEL3AYo2gG0BoZIYBCMcCIH0E3QAAILalZlxAZFHAREoAAAAAESLTDQAAgOgFFmsE3QBiOaQsDCsDALKFoBtAaASpAICMBRZr1HQDAAAAESPTDQAAgOgFFmsE3QBCo6YbAJCxIN4tAwm6AYRGkApAOBYA6SPoBgAAsR314gIie4ooLwEAAAiPQBVIH0E3AABIC5luZCSwWKNlIAAAABAxgm4AAAAgYgTdAAAAQMSo6QYAAEDkimJe003QDQAA0sLkQ2QkYHEcAACA0OheAqSPoBsAAKSFQBUZCSzWCLoBxDK7JQQOQGYK6VjAcQDZQtANIDROTgCEYwEyElisEXQDAIC0kOkG0kfQDSCWJ1rhZAtkhvcOMlFEphsAACCeF+BcQCBbCLoBhMbJCQCQscBijaAbAAAA0Qss1gi6AcRySFnI3AMAsoWgGwAAAJErinmmuzjXOwAAAAAUOjLdAAAAiF5QZHFG0A0gNGqgAQDIDEE3AAAAohdYrFHTDQAAAESMTDcAAAAiVxTzTDdBNwAAAKIXWKxRXgIAAABEjEw3AABIC52MkIkiMt0AAAAAokSmGwAApGXe529boSBrn0WBxRpBN4BYnmiFky0AIFsIugGERpAKAMhYYLFGTTcAAAAQMTLdAEKjvAQAkKkiMt0AAAAAokSmG0BoZIYBAMgMQTcAAACiF1isUV4CAAAARIxMNwAASAulZshEUcwz3QTdAEKjewmAQjsWcBxAthB0AwiNkxMAIGOBxRpBN4BYZreEiwgAQLYQdAMIjSAVAJCxwGKNoBsAAKSFC3BkooigGwDCobwEQKEdCzgOIFsIugGExskJAJCxwGKNoBsAAKSFC3AgfQTdAGI5pCwEDkBmCulYwHEge4pinulmGXgAAAAgYmS6AYRGRggAkLHAYo1MNwAAAGJlwoQJ1qJFC6tdu7Z16NDBFi9eHOr3Zs6caUVFRdajR4+0/yZBNwAAALKT6Q4iuqVh1qxZNnjwYBs5cqQtWbLEWrdubd26dbMvv/xys7+3YsUKu/zyy+2www7L6OETdAMAACA2brnlFuvXr5/17dvX9t57b5s0aZLVqVPHpk6dusnfKS0ttbPOOstGjx5tu+yyS0Z/l6AbAAAAWeleUhTRraSkxL7//vtyN22raMOGDfbmm29aly5dyrYVFxe7rxctWrTJfb/qqquscePGdt5552X8+JlICQAA0sKkauTbRMqxY8e6LHQqlY+MGjWq3La1a9e6rHWTJk3KbdfXS5curfK+X3rpJbvnnnvsrbfe+k37SNANIJa9eYXAAchMIR0LOA4UhmHDhrk67VS1atX6zff7ww8/2Nlnn21TpkyxRo0a/ab7IugGEBonJwDCsQD5lumuVatWqCBbgXO1atVs9erV5bbr66ZNm1b6+eXLl7sJlMcff3zZtkQi4T5Wr17dli1bZrvuumuofSToBgAAaSHTDV/VrFnT2rZtawsWLChr+6cgWl/379+/0s/vueee9u9//7vctuHDh7sM+G233WbNmzcP/bcJugEAQFoIVOHzMvCDBw+2Pn36WLt27ax9+/Y2btw4W7dunetmIr1797ZmzZq5OnH18d53333L/X6DBg3cx4rbfw1BNwAASAuZbvisZ8+etmbNGhsxYoStWrXK2rRpY3Pnzi2bXLly5UrX0WRLI+gGEMsTrXCyBYAsCixvqJSkqnISWbhw4WZ/d9q0aRn9TYJuAKERpAIAkBmCbgAAAMSmpjtXCLoBAAAQvcBijWXgAQAAgIiR6QYQGhMpAQAZCyzWCLoBhEaQCgBAZgi6AQAAELkiizeCbgChUV4CAEBmCLoBhEaQCgDIWGCxRvcSAAAAIGJkugEAABC5ophnugm6AQBAWig1Q0YCizXKSwAAAICIkekGAACx7WRE1j6LAos1Mt0AAABAxMh0A4hldkvIcAFA9hTFPNNN0A0gNIJUAAAyQ9ANAACA6AUWawTdAEKjvAQAgMwQdAMIjSAVAJCpIjLdAAAAQMQCizVaBgIAAAARI9MNAACAyBWR6QYAAAAQJTLdAEKjewkAIGOBxRqZbgAAACBiZLoBhEZmGIBwLEBGAos1gm4AoVFeAqDQjgUcB5AtBN0AQuPkBADIVBGZbgAAACBigcUaEykBAACAiJHpBgAAaaHUDJkoCuKd6iboBhDLyVNC4ABkppCOBRwHkC0E3QAAAIheYLFG0A0gNDJCAABkhqAbAAAAkSuKeaab7iUAAABAxMh0AwCAtFBqhowEFmsE3QAAIC10L0Emigi6AQAAwiNQBdJH0A0AANJCphsZCSzWmEgJAAAARIxMNwAASAvZYWSiiEw3AAAAgCiR6QYAAGmhphsZCSzWyHQDAAAAESPTDQAAgMgVxTzTTdANAACA6AXxjropLwEAAAAiRqYbAAAAkSuKd6KbTDcAAAAQNTLdAGLZJkxoFQYAWRRYrJHpBgAAACJGphtAaGSGAQCZKkpYrBF0AwiN8hIAADJD0A0gNIJUAEDGAos1gm4AAABEroigGwAAIDxGvYD0EXQDAIDYzu/gAiKLgninumkZCAAAAESMTDcAAAAiVxTvRDdBN4B4DikLw8oAgGwh6AYAAGnhghUZCSzWCLoBhMaJFgCAzBB0AwCA2JaakUzInqKYZ7rpXgIAAABEjEw3AAAAohfEO9VN0A0AANJCSQYyURTvmJugG0A86ziFwAHITCEdCzgOIFsIugGExskJAJCxwGKNiZQAAABAxMh0AwAAIHJFZLoBAAAARIlMNwAASAvzO5CRRLxT3QTdAGLZsUAIHIDMFNKxgOMAsoWgG0BonJwAABkLLNYIugEAABC5IoJuAACA8Bj1AtJH0A0AAIDoBfFOdRN0AwCAtDCREkgfQTcAAAAiVxTvRDeL4wAAACBeJkyYYC1atLDatWtbhw4dbPHixZv82SlTpthhhx1mDRs2dLcuXbps9uc3hUw3AABICyUZyEhgeWHWrFk2ePBgmzRpkgu4x40bZ926dbNly5ZZ48aNK/38woULrVevXtaxY0cXpF9//fV29NFH23vvvWfNmjUL/XeLgiBcVXvX4tPSe0QACk4h1XEKgQOQmUI6FhTacWB+Yrblq85HXx/ZfT/39BWhf1aB9oEHHmjjx493XycSCWvevLkNGDDAhg4d+qu/X1pa6jLe+v3evXuH/rtkugEAQKwDVWRHUYTdS0pKStwtVa1atdwt1YYNG+zNN9+0YcOGlW0rLi52JSOLFi0K9bfWr19vv/zyi2277bZp7SNBN4DQONECEDLdyEgiurseO3asjR49uty2kSNH2qhRo8ptW7t2rctUN2nSpNx2fb106dJQf+uKK66wHXfc0QXq6SDoBgAAaSFQRb4ZNmyYq9NOVTHLvSVcd911NnPmTFfnrfrudBB0AwAAwOvyklpVlJJUpVGjRlatWjVbvXp1ue36umnTppv93ZtuuskF3c8884y1atUq7X0k6AYAAGmhvAS+qlmzprVt29YWLFhgPXr0KJtIqa/79++/yd+74YYb7Nprr7V58+ZZu3btMvrbBN0AAACITcvAwYMHW58+fVzw3L59e9cycN26dda3b1/3fXUkUStA1YmLWgSOGDHCZsyY4Xp7r1q1ym2vW7euu4VF0A0AAIDY6Nmzp61Zs8YF0gqg27RpY3Pnzi2bXLly5UrX0STpzjvvdF1PTj311F+dqLk5BN0AAACIXpAnqW4zV0qyqXISTZJMtWLFii3yNwm6AcSyjlOo5QQAZAtBN4DQCFIBAJkqyp9Ed04QdAMAACBW5SW58P+rxAEAAABEgkw3AAAAIlcU4TLwPiDTDQAAAESMTDeA0OheAgDIWBDvmm6CbgAAkBYuWIH0EXQDCI0TLQAgY4HFGkE3AACIbakZyQRkC0E3AAAAIldETTcAAEB4ZIeRkYCgGwAAIDTKS4D0EXQDAAAgegmLNRbHAQAAACJGphsAAACRK4p5TTeZbgAAACBiZLoBxHLylDCBCgCyKIh3ppugG0BoBKkAAGSGoBsAAKSFC3BkJCDTDQAAAEQrYbFG0A0AAGI7v4OsPbKFoBsAAACRK4p5eQktAwEAAICIkekGEMshZWFYGQCyKIh3ppugG0BoBKkAAGSGoBsAAADRC8h0AwAAhMaoF5A+gm4AAABELyDTDQChMJESQKEdCzgOZFHCYo2gG0BonJwAAMgMQTcAAAAiVxTz8hIWxwEAAAAiRqYbQCzrOIVyGQDIooBMNwAAAIAIkekGEBqZYQBAxhLxznQTdAMAgLRwAQ6kj6AbAADEdn4HFxBZFJDpBgAAAKIVEHQDAACERnYYSB9BNwAASAvlJchIQKYbAAAgNAJVIH0E3QAAIC1kupGRRLwz3SyOAwAAAESMTDcAAACiFyQszsh0AwAAABEj0w0glnWcQi0nkBneO8hIEO+aboJuAKFxogVQaBfgHNeQLQTdAAAAiF6CTDcAAAAQrYCgGwAAIDRKMoD0EXQDiGUdpxA4AJkppGMBx4EsCsh0A0AonJwAAMgMQTcAAIjtBXghZe3zXkCmGwAAIJaBaiFdQMj8eC/6mNcIugEAABC9RLyvCAi6AQBArLPDQDYQdAOI5ZCyEDgAmSmkYwHHgSwKqOkGAAAIjUAVGQkIugEgFE60AIRMN5A+gm4AAABELxHvTHdxrncAAAAAKHRkugHEckhZGFYGgOwJAloGAkAoBKkAAGSGoBsAAADRS1DTDQAAACBCZLoBAAAQvSDemW6CbgAAAEQvEe+JlJSXAAAAABEj0w0AAIDoBZSXAAAAhEb7UCB9BN0AACC2C2VxAZE9ATXdAAAAAKJEphtALLNbQoYLALIooKYbAEIhSAUAIDME3QAAIC1cgCMjCTLdAAAAsSw14wIiiwImUgIAAACIEJluAAAARC6gvAQAACA8SjKA9BF0AwCAtFDTjYwE1HQDAAAAiBBBNwAAALJS0x1EdEvXhAkTrEWLFla7dm3r0KGDLV68eLM/P3v2bNtzzz3dz++33342Z86ctP8m5SUAACAtlGTAZ7NmzbLBgwfbpEmTXMA9btw469atmy1btswaN25c6edfeeUV69Wrl40dO9aOO+44mzFjhvXo0cOWLFli++67b+i/WxQE4dbk7Fp8WnqPCAAAAFk1PzHb8lXXCGPJJ36630pKSsptq1WrlrtVpED7wAMPtPHjx7uvE4mENW/e3AYMGGBDhw6t9PM9e/a0devW2RNPPFG27aCDDrI2bdq4wD20II/8/PPPwciRI93HQlBIj6eQHovwePJXIT0W4fHkr0J6LMLjyV+F9Fjy1ciRI5VELnfTtopKSkqCatWqBY8++mi57b179w5OOOGEKu+7efPmwa233lpu24gRI4JWrVqltY+hM93Z8P3331v9+vXtu+++s3r16pnvCunxFNJjER5P/iqkxyI8nvxVSI9FeDz5q5AeS74qKSkJlen+/PPPrVmzZq5k5OCDDy7bPmTIEHv++efttddeq3TfNWvWtHvvvdeVmCRNnDjRRo8ebatXrw69j9R0AwAAwGu1NlFKkk/oXgIAAIBYaNSokVWrVq1ShlpfN23atMrf0fZ0fn5TCLoBAAAQCzVr1rS2bdvaggULyrZpIqW+Ti03SaXtqT8v8+fP3+TPe1FeomGBkSNH5v3wQBwfTyE9FuHx5K9CeizC48lfhfRYhMeTvwrpsRSCwYMHW58+faxdu3bWvn171zJQ3Un69u3rvt+7d29X960WgTJw4EA74ogj7Oabb7Zjjz3WZs6caW+88YZNnjw5rb+bVxMpAQAAgKipXeCNN95oq1atcq3/br/9dtdKUDp16uQWzpk2bVq5xXGGDx9uK1assN13391uuOEG6969e1p/k6AbAAAAiBg13QAAAEDECLoBAACAiBF0AwAAABEj6AYAAAAiRtANAAAARIyg+zf6/vvvQ9+AbPC5IdFHH31k8+bNs59++sn7x5Kkx1AIj6NQvPDCC7Zx48ZK27VN3wOAqNAy8DcqLi62oqKiUD9bWlpqPvnvf//rHttOO+3kvl68eLHNmDHD9t57b7vgggvMR8uXL7e//e1v7uNtt91mjRs3tqeeesp+97vf2T777GO+OOecc2zChAm29dZbl9uu/qFnn322vfjii+aTr776ynr27GnPPvuse819+OGHtssuu9i5555rDRs2dAsS+Oaee+6xW2+91T0WUV/XSy+91M4//3zzkVZs00XRl19+6T5Pdfjhh5svtPzzF1984d77FV+D2ubbcfq+++6zSZMm2SeffGKLFi2ynXfe2S300bJlSzvxxBPNN1r1T7eqXmdTp061fKfjVdiY4Ouvv458f5BfqudqJaCwbrnlFstnzz33XLmAZ+jQoS4gSi4NqoPgvffeW7aqkU/OPPNMF1wriFPz+K5du7rA9IEHHnBfjxgxwnzy/PPP2x/+8Ac75JBDXEbr2muvdSfZt99+2wVIDz30kPlC+9yqVSu7//77y15rep39+c9/tiOPPNJ8M2jQIKtevbqtXLnS9tprr7LtCsR1vPAt6NZ7Q8euAQMGlDsW6HHqMV511VXmk1dffdUdDz799NNKWXsFGD4Fqtr/qoIiBd0VL2Lz3Z133ulea7qY0/Es+Tw0aNDABd6+Bd2jR4927w2tErjDDjuEDl7zif7vqa+pa665xrp161buOKDRvL/+9a853EvkTJADnTp1KnerV69eUKdOnWD//fd3t6233tpt69y5c+CTI488MpgxY0al7Q888EBwxBFHBL5p0KBBsHTpUvf5bbfdFnTs2NF9Pm/evKBly5aBbw466KDg5ptvdp/XrVs3WL58ufv8tddeC5o1axb4ZMOGDcHll18e1KxZMxg2bFhw2mmnucc0efLkwEdNmjQJ3nrrrUrPjT7qeOCbRo0aVXks0Lbtttsu8E3r1q3da+w///lP8M033wTffvttuZsPTjrpJHcrLi4OunfvXva1bieccELQokWLoFu3boFP9tprr+DRRx+t9L7597//7eXrrGnTpsH06dODQnHyyScHd9xxR6Xt2nbiiSfmZJ+QW9VznR1WNmibbbZxWToNy8g333xjffv2tcMOO8x8oitYDfNVpKt2H4eUf/nlF6tVq5b7/JlnnrETTjjBfb7nnnu64Vnf/Pvf/3blMRUp27127VrzSY0aNdzytXXq1LGrr77aZYmVyU9mU3yzbt0691iqGn5NvgZ9e+/ofV9R27Ztq6wnzncqkdFI0G677Wa+ql+/flmmW+ecrbbaqux7NWvWtIMOOsj69etnPlFJyf77719pu94zek/5ZsOGDdaxY0crFMpoX3/99ZW2H3PMMW5UHPGT84mUGjZW6UUy4BZ9riEZ34aUmzdvblOmTKm0/e6773bf841KSXQRofrg+fPnuwOFfP7557bddtuZbzTkWtXFwr/+9S9r1qyZ+RbUXXbZZe6APmzYMBdsn3zyyTZnzhzzkS6wp0+fXva1hpVVz3nDDTdY586dzTcqydLQf0WTJ0+2s846y3zToUMHV8/tM83l0G3kyJGunCz5tW533XWXex81atTIfKK67bfeeqvS9rlz55Yr0/KFklNVJUZ8pfPk448/Xmm7tvl4DsVvl5NMdyp19VizZk2l7dr2ww8/mE80aeqUU05xE/N0kkpOPlSW6OGHHzbfKKA76aSTXEa1T58+1rp1a7f9H//4h7Vv3958c8YZZ9gVV1xhs2fPLgvqXn75Zbv88sutd+/e5hNlUdevX28LFy50GTpl7xSgKvDW5MOJEyeaT7TvRx11lL3xxhsu2zVkyBB77733XKZbz5GPFNg9/fTT7vmR1157zdVz67WWOq8lX+etvPPOO2WfqzZdF3may7Hffvu5kZZUml/gCwXdGm3Q6J0mVKtWXZlvJRPq1atndevWNV/odfSnP/3Jfv75Z3cM0PnmwQcfdIksJXt8o8ehC1M9N3pNVXyd5et7ZXM16rqQ0HE6GRPoOKCLoqoSdCh8Oe9eohOQMqnKaicDOb0o//KXv7jsl8pOfOv4oQzX0qVL3dfKNlx00UVeZrpFE3N0YZQ6EqEJoyoFqDj7P98pmNMJatq0ae5xqSRDH3XS1TZ1NfDFeeedZ7fffnuliV/K2ivL+u6775pvvvvuOxs/frybJPrjjz/aAQcc4J4vTajyTdjsvC7+1LElnzszbeoUkfyebxMpNRlUo3a6ACopKbEPPvjAdcoZOHCg+7qqEsF8ponto0aNchcQsuOOO7pgT8eIQnrf5PN7ZXMUz+hY/f7775fFBJrwngzCES85D7qVrVOmUa2ANGQuCoZ0wFCG1bfZ5IVEvZL18kjW2upk9eijj7qDhmZj+0onWwWlCuxUD6lWboVEgYOPddDIL3q/h6U2db7o0aOHy2xrJEJD/LrIU9CtbKRqupMtHvOdsvUqxdCxuEmTJu5cqmOab8kQIE5yHnQnadJH8kp911139TbYVtZe9YEff/yxK2NQrbD6qKr27tBDDzWfHH300a5cQZn6b7/91k2g1HCfJh1qmO/iiy/O9S7GWiH1500tZaiY3apdu7bro+7zhYRGi5Sl03tIN+SOAu1XXnnF9thjDxd8J4NujeBpDQIFr75QQkQZVJ8ueuImuTaEYgIdn31dGwIFUtOdpCDbp7rAqqhuW0P7mii1ZMkSl3FMDpuPGTPGu0luegyqUxd1LlA2ReULepzqDetb0K3rSz0Odc+pauGFRx55xHxRaP1527RpU9aTN5kHSO3Rq4s99ezWBa2C8Hx3+umnuwVj+vfv70aMVIOvoE6PbebMmW7uh09UI6z3v+YLpNIIpebfaK6EL/S+r6oc5n//+58Lwn2ikkwdk30OupXYUXmf6un1+eb4dIyuam0INYjwdW0IeNy9RG+s5LLo+nxzN5/oDaXMoyZIpE4A0RtOAaxvlPFJnoQ0IUzPh+o8NTEsnaHnfKEAVRdFygxrspRaiKXefHLHHXe419mVV15ZrhZdwZ1aI/pGZUsq89EkKp2QdNPnykZqCF0nKGWKhw8fbj7QCTbZ8lSPTcG2RotU26njhG90sVNVhj7Z4ci3EbzUBUx0caeyDE2w7N69u/nkkksucRNcNRdCo10aMUq9+UDH3uQFdsVjss/HaFFbQL3f1f1LbSmTtICZFpxC/FTPhzdZoVi2bFmVyyHrMeqE6xv15H3sscdcBxP1G9VqeqIssbISPpZjKFPi24k1Dv15la2/7bbbys0VUJeMnXbaya3cpq4MGg1TgHHTTTdZvtPo1rbbbus+V6cCZbZVCnDssce6SeK+UdeSqia0br/99t717Nekfb3OVEqibhmaSK06brULVOcP3zoyiSbm+TrBVaUXVX1eCAppbQh4HHQn31g6MGiWtQ7cqQsV+Kpp06aul22LFi3KbX/ppZdczaBvVL6gE5KCbbVzSy68oqx3VQFfvtPFj4/Pw+b681YcVva1P69OTlUNkWtbMnOvEhRfAjx1K1LmUYG3nhOVlCQX/vKhPKaqx6PWjXrdpdI2dcvwiS7kNJIya9assk45mrivskDfzkO6+C5ESuwoiSUa7fJ1cmhybYiK7xsf14bAFpLL5TBLS0uDGjVqBB988EFQCMaMGRPsvffewauvvhpss802wYsvvhjcf//9wfbbbx/cfvvtgY+++OKLYMmSJe65StKy6e+//37gm2nTpgVnnHFGsH79+sB3U6ZMcUvXz5w50y2T/uCDDwbXXHNN2ee+adOmTdCnT5+gpKSk3FL32qbvyUsvveSW6vbBhAkTgurVqwcNGjRwS6gn3z86DnTq1CnwzfXXX++WFZ86dWqwYsUKd7vnnnvcNh33fDJjxoxNfu/yyy/P6r6gvO+++y744x//6N47RUVF7qbPzzrrrODbb78NfHPZZZcFhx56qDuPKib48MMP3XFsl112CUaNGpXr3UMO5DToFgWpixYtCgpBIpEoC3ySB4zatWsHw4cPz/WuIQhcsN2tW7egbt26wb777hvsv//+5W6+0QXdbrvtVvZa22mnnYK777478NHLL7/sAjhdoB511FHu1rhxY7cteXyYPn16cMMNNwS+eP3114NHHnkk+OGHH8q2PfHEE+6k6+OxbciQIe54Vlxc7G516tQJRo8eHfimfv36wZw5cyptv/TSS4OmTZsGPnrvvfeCp556Knj88cfL3Xxz+umnB7vvvnswd+5cF4Drps/32GOPoGfPnoFvlEQ4//zzyy4ilGTUe0cXFhs3bsz17iEHct4y8J///KdbjU7dGPbdd18rBFqERWUmGrZU3aBPK5xVpBUC//73v7ve1npcPs8kV0cJdS459dRTXSeG1O4YoolUPvZQ14RX9R3XUL9eb772UNcKtFroQ4uVJIeVk6sF+qyqbiw+UV2wXlvJlSjVok5lGJr46mMbxyeffNKVkjzxxBNlbVy14qaOZwsWLPCqpaPa0GnOjUqwUhcySr7WfKjpTqV5G5o/VLG9rlrxakEjH+erJBfN03OUujaEjuG+lTNhCwhyTMOvNWvWdFd/yqI0bNiw3M0n9913X7Bu3bqgUKhMQVfmxx13nHuO9PH3v/+9yxSdc845gW+UmVPJTyHo2rVrcOedd7rPv/nmm6BJkyYu06330MSJEwNfFUrGTjTqsM8++7j3jm76XGVBPqpVq1bw8ccfB4XigQcecOeXN954I7j44ouDHXfcMVi2bFngGx2TTzzxxGDNmjVuBO8///mPO8a1b98+eOGFFwLfNG/ePHjnnXcqbX/77bddOZ1vBgwYUOX2H3/80csyM/x2Oe/Tndq6yXeacKiFZE444QT74x//6DKOPi0tXpF6i6tPt5biVrZR3SU0IeTCCy/0cmluTQbzsetKHHqoV5WxS80M+5ax03OgBaSUQU1OQNbESh0jNGp01VVXmU80CqnnqOKEMF9pBEUdpdTOVRP51U9Z3Zp8o9eUWmmq84raueqmLLH6qqujiY4JPlFL0MGDB7tOU2pMkOyco44/6mLk46hKw4YNXcOIJGXrlbVHPOW8vKSQaFledSpQ26nHH3/cDf2fdtppbiizY8eO5hsN9b333nuuG4tWcdMyyRpi1vCy+oz60kki9QCo/tbqK1yxw4xv9NpaunSpW9VMZTPql6zyGA1jqizDp1X15Pjjj3cXqHfffbcL7F577TX7+uuvy1oEJnte+0KBnHpy9+rVq9x2HRsUiPvWLkzHtWHDhtnVV19tbdu2rbRicL5fzCqQq4pWDT7ggAPcKshJuljyhQI6XYDrPaPHoPdP586d3SqIOlb7cBxQuUXqBbbaN2phOR3bRBepKmNSSYZv613oedCxa8iQIW6dCJXQKRlXvXp1tyqlrytvI3M5z3SnLpOqj8qm+rpMqt5Ixx13nLvpYKdFMdSjUwdBtalKLnPv0wFdBwlReyPVDetArgyRDwfzijT6oP3WyUlBa+oCRqIgzxeF1kO9YsZOAbjPGbtffvnFLVRUkQJWXZz7JtnbXqN4qQGSL/2gN/X60ftIC7Ulv+9b3b1GINT2UEF3hw4d3PwoLcKihaV8aY/ao0cPK1Q61+iCVTGAjmu66NYFhBJABNzxVD3flknVIhmFsEyqgjpd0aovr1ZvVHbYN1roRytpKdBWxn7gwIEuMNI29e32TSGVMhVaD3UFbckJkwq8P//8c5exV5/uZL9en2jlU00Or5g1VTCkkS/faAKyz3zf/1RaaVLBtoI4lWMkEyAqWVLCR5lVjUyqD7kPfJrAnolWrVq5Sbtdu3Z1F0b6nAmU8ZXz8hIFCwroNPynk66CbV2hawU6LTv+v//9z3ySzHCrC4NmwquOWEPMOtH6NCs+mfnVim1a/CKRSLgsyiuvvOKG+XSwVyYcuaNaR5X4tG7d2p2ARe8bZbp9e60pUFApibJeupjQxapeYwpS33zzTTfKku9SSxiUzZ42bZobrTvooIPcNpXMaKi8d+/erswJuVstVBd5yRVDU493Gq3M95EijQLpfa/klM6Vr7/+uguyUx+Hjs2+Ze0LRcVymSQl3/ScpQbcvpXLoACCbrXT0+QpDY+lBt0rVqxwgYOCPp+W5NVVrLLcqrNVoJ3MQCK/6HVVsQVivp9sC5lKZDTBSBfaarepjJ1aByYzdppDkO80hByGTsgaMfKRkgpVtQ9VNs8XGlnVHIJLLrmk3HbN9fjHP/5hc+bMsXym94T2UVlTXWyvXr3azSEoBLoY0gTxTbWp9aEEMHXSZNyz/MjD8pJCWiZVGQgdLHzuWqL6xrB8C1IV1F1xxRXuOfrqq68qfT/f61ILWWpvcdXZapKobxm7QiphqGjNmjXWt29fN9emKj69dzTiUNVkyU6dOtmVV15p+e6UU06xI444wnWQ0ntDcwc2db5RxxmfKGDVZFCNemmkS8+HEnCav6KSOh8kA+lkf3tdkCrOAfIi6FZ2WIGQZpHrAKIyBr1QL7/8cjcM6xOVlPhOB4dfC3J8mTxVkWaQKzBSra1qbidMmGCfffaZ3XXXXXbdddflevdQQcXhf+SOOi9oArUCVgWnKqFThvWaa66xm2++2XyizhhVTWbV5FctWJLvVHKVHBHSJON+/fp5v4BU6jl0ypQpduyxx9qoUaNcaaYmIypwffXVV93j9YUuhI4++mg3n4ugG3kTdKsXtPpAq/ZZQZxW1NNH1XXqSjffqS3YBRdcYLVr13afb44PB4xCztZp9dPp06e7oEFZO9URK6uqyXo62Ps4wQ35Q4GQ6rg1AqTPN8e31VxVDqM2qMqqqqRB7xlNDNNjVYcZBUm+aN++vQtcK9bVq7xE3WV8kOzzrPkOmuBeKEG35qlo4n6y9FT196JyMx/7dBdaf3sUQNCt9ka6stUbSpOlUpdJ9YHqzxSsKehOLlZSFWWGfQi6NWxZqFSukGyjpWAhWR+o1nS+LSaD/FO/fv2yUSJ9XkhUmqVJYKKSH5Wb/P73v3cBkm+TwZSd79Kli5s/lOzCpEnvmpCo7j8+UavdQqLWuio31QRkZbj1fKiPup4btdrzjV5rGrX3tb89CjDoTtKbLNkM3yeffPJJlZ8XAh3QlW1Qd5lUKgXShKo+ffqYTxRw6znS60yTdFXbrayXMuAM/2FLBkATJ050pXLJk2yyLnWvvfYqV7/uC7VvVOtGLSqlbjkqydLnyg77tjqt2tOqL/yNN97ojgHqJqHyBbWo9SXZU6i07oAugDRJVItIaW0FPS+aVJlci8Anvve3RwF2L9nUSmF6QSp7rOH/E0880Yv6zpdeesllTQuFMlk6uVbsyqDe6iqp8a1/skYiVGenEYdnnnnGdTDQy1+1nJpYpWFaYEtQLadKTC666CJXC62LPC3GpJUo9VrzbWTl/vvvd3XQ55xzjitpUHmDJiNrpPLee++1nj175noXUYBUx51sU6vjtW90rozryDLyNOhWQKfhSV3xKZsiahWm4EgnKgV2CsAV0KreO5/pBKSOK8m+3D6tplkVXfSoi0TFJdOVtVPGzodJR5ujvqkKIHRh51PLM+Q/LfCjE66OAerGoPphdWR6+OGHXRcGHxfLStIpQ+99HRs0aqTH6kNXpuRQ/q91aGLIP3c0P6BJkyZ27rnnlts+depUV9KkpguAz3JeXpLMYmtoNnmw0+SJ888/32WNNTM7ufKeevnmM62iN3PmTLfUq7phKJBT8K0gXLVqvlENp1Y/qxh0qxYydTEGn2joUjctl67h/4oHdmBLUPlVcnKb6lKV9dYERC2Uo4s9H2mYX6NFH374ofta2Ud1NdGxOt+pDj25oMymOjQx5J97GlmdMWNGpe26eE12OvNRIfS3R4EE3aqr07LiqdkFTUJSuyAN0WrIX5khfZ7vlPHp37+/u6l2WAcPDb0OGzbMLanu24IYulhQKYaCB+2/KHun50QHQN+oB6yWSlYHhmSPWyAKGj1RDbdqVJUsSNaj6mLPx0yqjsEqi1GdbXLBL9VF63EpmND7Kp/p2JssUVSCR92yKva21kW4Hgty272kqjkCWvxHF02+KaT+9thCghzbeuutg+eee67Sdm2rW7eu+3z58uXBNttsE/hm48aNwT//+c+gTZs2QXFxceCbkpKS4PTTTw+KioqCGjVquJseR9++fd33fNO0adNg+vTpud4NxMDs2bPL3i9du3Yt2z5mzJjgmGOOCXzTqFGjYMaMGZW2a9t2220X+ETPyerVqyttX7t2rZfH6UKy2267Bffdd1+l7Tput2zZMvDNmWeeGRxyyCHB66+/7mKdp59+2j2+PfbYI3jiiSdyvXvIgbwoL1H9lhZYOPDAA902tQdSm50ePXq4rxcvXuwm9flCi/uo7/NDDz3klhvXY1Stmm9Uo64luNX2SPWoyVn+6tHrIw3tdezYMde7gRg49dRTXXmcsnPq9pGkFnXKfvtGk401QlSR2qBVtdBMPkuWkVSkdrWax4LcUTmpSpb0ejvyyCPdNpUDamEzrVLpm0Lqb48CmUipA52GKLVoSfLgXb16ddeOTvWDarn11ltvue1t2rSxfDZ06FAXpKq2W28s1XMr4K5Tp475yuc6zopUD6gWiD4usgDkkspK1H2l4vLpSo5oUqVWd813yU5Zt912mwvuUo/LGubXapsqOVHSBLmhcETnUS00l6x/1oWQjt2+LAOfSsF1cl6UAm6VnKplpcpPVaeuWm/ES86D7tTgWys3JfspKzjyjd5MCrRPP/10L2b0Z1rHOX78eHehlO91nBWpFl0Xd8rW66YgIlXFgALA/6NjgN47qoXWZFBRkKoa6N69e5d7L+Xr+yjZ+lTzUnQ800hekj5XYKSLCHp150c8oA4/Gl3V8+Hjwjii0XuNFKs3v3p1axKvMty6qNBI+PLly3O9i4hr0O07DYddeOGFLotaKEu+avKKDg6aUJlK3Vl0ElbPYZ9U7DeeSsPNvk10BfLhvePb+0gT25Tt9nFCK8z7/vZaCVkXedOmTaO/fQzlPOjW8sJqr7epNm7J7LcP1HVFpTCFEnTrqlz19RUzP+qjrpUctegHAAD4dSon8am/PQow6FYWVcN9Z599dpVt3HxaJVB16Ko793G52kKt4wQAINeSoRatauMt50G3sqlPPvmkq4f2nWq31IVFHQo0q1+TQFOp57VPCqGOEwCAXCmkZgQogKBbpRhz5sxxy4r7bnNlJbq69alUptDqOAEAyKZCa0aAAgi6NdFAfSy1cqPPrfUAAAAKtRkBfrucL46jcgy1zWnSpIlr2VSxjduSJUtytm8AAABxX1QKBRJ0J1edLARaWXNzpk6dmrV9AQAAuaMGEXfeeWelOU+TJ092a3ogfnIedI8cOdIKxTfffFPpKvfdd991rfWSS9oCAIDClFz5NDnf6e6777ann366ymYEiJ+c13SLgtLk6kx/+ctfbNttt3VlJSo5adasmflMfccvvvhi23XXXW3IkCG53h0AABARGhAgr4Pud955x7p06eIWllmxYoUtW7bMLQM/fPhwdzWolnW+02Pq1KmTffHFF7neFQAAAORAseXBUIyWSFUPy9q1a5dt7969u73wwgtWCJTBZ9IEAABAfOW8plvLjN91112VtqusZNWqVeZrLZdoEEHZbS3+o9UqAQBAPPz88892xx132HPPPWdffvmlKzdNRXe2+Ml50F2rVi37/vvvK23/4IMPXI9Ln/zrX/8q93VxcbF7DGqL+GudTQAAQOE477zz3CTKU0891dq3b88S8Mh9TbeWQv3qq6/s73//u5tAqRrvatWquVaChx9+uI0bN858sX79epfdTi7/rhr1xx57zK222a1bt1zvHgAAyBLNVdOK24ccckiudwV5Iuc13coC//jjj9a4cWP76aef7IgjjrDddtvNttlmG7v22mvNJ7pQuO+++8o6sqhFkB6ftqtXJwAAiAeVySqWAfIm05308ssv29tvv+0C8AMOOMB1NPFNo0aN7Pnnn7d99tnH9eZULZdKTh5++GEbMWKEvf/++7neRQAAkAVPPfWUWwZ+0qRJtvPOO+d6d5AHcl7TnaThl+QQjLLEPlJ5SfKqVnVcJ598sqvrVsb7008/zfXuAQCALNES8JpMqTbIderUsRo1apT7/tdff52zfUNMg+7rr7/eWrRoYT179nRfn3766S4z3LRpU1cL1bp1a/OFymJUw33SSSfZvHnzbNCgQW67Zi3Xq1cv17sHAACypFevXvbZZ5/ZmDFj3GJ/TKREzstLWrZsaQ888IB17NjR5s+f74LuWbNmuYmVWhxHGWNfaFXNM88800pLS+2oo44q2/exY8e6nuMaagIAAIVP2e1FixZ5lTxEgWe61Yu7efPm7vMnnnjCBd1HH320y3536NDBfKK2QIceeqjrzZ36JlMAruw3AACIhz333NM1iADypntJw4YN7b///a/7fO7cuWUTKJWAV8bYNyqL2X///V0td5L6c+rNBwAA4uG6666zyy67zBYuXOhaI2tNktQb4ifnmW5NNlRJxu677+5elH/4wx/cdnX9UI00AACAb4455hj38cgjjyxXz62kor72MbEIz4PuW2+91ZWSKNt9ww03WN26dd12lWhccsklud49AACAtGn5dyCvJlICAAAUohdffNHuuusuW758uWu2oAVztIiemkhoDhjiJec13ffee689+eSTZV8PGTLEGjRo4LqZ0NsaAAD4SO2Pu3XrZltttZUrmS0pKXHbv/vuO9dGEPGT86BbLzy9IEWtdSZMmODKTLS6Y7LPNQAAgE+uueYatxrllClTyi2Mo4UAlyxZktN9Q0xrulXLnZwwqYVlTjnlFLvgggvci7JTp0653j0AAIC0LVu2zA4//PBK2+vXr+/tytvwPNOtiZPqWiJaTKZr167u89q1a9PfEgAAeEkthD/66KNK21966SW3NDziJ+eZbgXZ559/vutt/cEHH1j37t3d9vfee891NQEAAPBNv379bODAgTZ16lTXIvDzzz93ZbSXX365/fWvf8317iGOQbdquIcPH+7KTDTpYLvttnPb33zzTevVq1eudw8AACBtQ4cOtUQi4ValXr9+vSs1qVWrlgu6BwwYkOvdQw7QMhAAACAiGzZscGUmP/74o+29995l65EgfvIm6NZV4MqVK92LM1WrVq1ytk8AAABAQZSXrFmzxs455xybO3duld9nmVQAAAD4LufdSy699FLXKP61115z/boVfGvBnN13393+8Y9/5Hr3AAAAAP8z3c8++6w9/vjj1q5dOysuLradd97ZdTSpV6+ejR071o499thc7yIAAADgd6Z73bp11rhxY/d5w4YNXbmJ7LfffqzYBAAAgIKQ86B7jz32cKs2SevWre2uu+6yzz77zC2dusMOO+R69wAAAAD/u5fcf//9tnHjRjeZUr25jznmGLdCZc2aNV1td8+ePXO5ewAAAID/QXcq7YqWfl+6dKn97ne/s0aNGuV6lwAAAAD/y0vknnvusX333ddq167t6rp79+5tjz32WK53CwAAACiM7iUjRoywW265xS2JevDBB7ttixYtskGDBrnFcq666qpc7yIAAADgd3nJ9ttvb7fffrv16tWr3PYHH3zQBeJr167N2b4BAAAABVFe8ssvv7ge3RW1bdvWTbAEAAAAfJfzoPvss8+2O++8s9L2yZMn21lnnZWTfQIAAAAKqrxEJSTTp0+35s2b20EHHeS2aUl41XNrQmWNGjXKfla13wAAAIBvch50d+7cOdTPFRUVuSXjAQAAAN/kPOgGAAAACl3Oa7oBAACAQkfQDQAAAESMoBsAAACIGEE3AAAAEDGCbgAAACBiBN0AAABAxAi6AQAAAIvW/wWN2C1e+KA82wAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1000x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv('titanic.csv')\n",
    "df.columns = df.columns.str.lower()\n",
    "# 检查缺失值并进行处理\n",
    "\"\"\" \n",
    "先分析数据，再有的放矢的处理缺失值\n",
    "以下代码是一个固定的模版，可以查看和可视化缺失值热点，辅助分析\n",
    "\"\"\"\n",
    "# 1. 缺失值模式分析\n",
    "# 缺失值统计和比例\n",
    "missing_data = df.isnull().sum()\n",
    "missing_percent = 100 * missing_data / len(df)\n",
    "\n",
    "missing_table = pd.DataFrame({\n",
    "    'Missing Count': missing_data,\n",
    "    'Missing Percentage': missing_percent\n",
    "})\n",
    "missing_table = missing_table[missing_table['Missing Count'] > 0].sort_values(\n",
    "    'Missing Percentage', ascending=False)\n",
    "print(missing_table)\n",
    "\n",
    "# 可视化缺失值模式\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.heatmap(df.isnull(), yticklabels=False, cbar=True, cmap='viridis')\n",
    "plt.title('Missing Data Pattern')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "4f289734",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. 处理缺失值\n",
    "\"\"\"\n",
    "age是数值型，以下用中位数填充会影响方差，以及一些和age相关联的分析，需要综合取舍\n",
    "可以通过分组，将不同分组的age中位数填充以增加准确度；\n",
    "也可以在进行age相关分析时，再去处理age缺失值，drop掉也可以，因为age还是有相当一部分没有缺失的。\n",
    "\"\"\"\n",
    "# Age: 用中位数填充\n",
    "df['age'] = df['age'].fillna(df['age'].median())\n",
    "\n",
    "\"\"\"\n",
    "就缺两个，众数填充，或者更复杂一点，通过分组后取众数对缺失值进行预测，更权威\n",
    "\"\"\"\n",
    "# Embarked: 用众数填充\n",
    "df['embarked'] = df['embarked'].fillna(df['embarked'].mode()[0])\n",
    "\n",
    "\"\"\"\n",
    "大量缺失，没有登记，也是一种数据特征，可以增加一列记录此特征，原列可以用unknown填充\n",
    "\"\"\"\n",
    "# Cabin: 转换为是否已知\n",
    "df['cabin_known'] = df['cabin'].notna().astype(int)\n",
    "df['cabin'] = df['cabin'].fillna('unknown')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "1c319d4c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 2. 处理重复值\n",
    "duplicates = df.duplicated().sum()\n",
    "if duplicates > 0:\n",
    "    print(f\"发现 {duplicates} 个重复行，将被删除\")\n",
    "    df.drop_duplicates(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "05cec772",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 13 columns):\n",
      " #   Column       Non-Null Count  Dtype   \n",
      "---  ------       --------------  -----   \n",
      " 0   passengerid  891 non-null    int64   \n",
      " 1   survived     891 non-null    category\n",
      " 2   pclass       891 non-null    category\n",
      " 3   name         891 non-null    object  \n",
      " 4   sex          891 non-null    category\n",
      " 5   age          891 non-null    float64 \n",
      " 6   sibsp        891 non-null    int64   \n",
      " 7   parch        891 non-null    int64   \n",
      " 8   ticket       891 non-null    object  \n",
      " 9   fare         891 non-null    float64 \n",
      " 10  cabin        891 non-null    object  \n",
      " 11  embarked     891 non-null    category\n",
      " 12  cabin_known  891 non-null    int64   \n",
      "dtypes: category(4), float64(2), int64(4), object(3)\n",
      "memory usage: 66.8+ KB\n"
     ]
    }
   ],
   "source": [
    "\"\"\" \n",
    "数据类型进行转换后，内存存储的压力应当变小\n",
    "所以有限选项应转为category类型\n",
    "数字也可以适量的压缩，如年龄int64 -> int8，passengerid int64 -> int32（甚至int16）\n",
    "用df.info(memory_usage='deep')获取内存使用情况的变化\n",
    "\"\"\"\n",
    "# 3. 数据类型优化\n",
    "df['survived'] = df['survived'].astype('category')\n",
    "df['pclass'] = df['pclass'].astype('category')\n",
    "df['sex'] = df['sex'].astype('category')\n",
    "df['embarked'] = df['embarked'].astype('category')\n",
    "\n",
    "df.info()\n",
    "`实际操作下来，内存变化有，但是后续category使用不方便`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f550e1aa",
   "metadata": {},
   "source": [
    "## 描述性统计分析\n",
    "- 计算生存率总体统计\n",
    "- 按性别、舱位等维度分析生存率差异\n",
    "- 年龄分布的可视化分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "9ac710c3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "总体生存率: 38.38%\n",
      "\n",
      "生存情况统计:\n",
      "survived\n",
      "0    549\n",
      "1    342\n",
      "Name: count, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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dj1UFWrVqlQ4++OD8c9WiRYvyXNaI11haq/62WLWgR48eTb7vAAC0wIA95JBD0le+8pV05513pt/97nfpnHPOSf/5n/+Zjj322DzXddKkSfl+sQrB4MGD8zzYWEJr5syZaerUqWnt2rVp7NixaciQIc2x+wAAtLQpBP/xH/+Rl9E69dRTc5zG9YjVmOc6YcKEvNZrjNLGqOz06dPzY+LkBePHj0/HH3982nnnnfPSW9U1YgEAaDnqKvVPdbWNWLJkSV4iK5bU6tat2zrbYlrBwoUL08CBA3PIbqoY2Y3VCOKArljBgE30r2XLYKvb9n4VAdDENrXXtsmA3RoE7OskYGkqLeNXEQBboNea7VSyAADweghYAACKImABACiKgAUAoCgCFgCAoghYAACKImABACiKgAUAoCgCFgCAoghYAACKImABACiKgAUAoCgCFgCAoghYAACKImABACiKgAUAoCgCFgCAoghYAACKImABACiKgAUAoCgCFgCAoghYAACKImABACiKgAUAoCgCFgCAoghYAACKImABACiKgAUAoCgCFgCAoghYAACKImABACiKgAUAoCgCFgCAoghYAACKImABACiKgAUAoCgCFgCAoghYAACKImABACiKgAUAoCgCFgCAoghYAACKImABACiKgAUAoCgCFgCAoghYAACKImABACiKgAUAoCgCFgCAoghYAACKImABACiKgAUAoCgCFgCAoghYAACKImABACiKgAUAoCgCFgCAoghYAACKImABACiKgAUAoCgCFgCAoghYAACKImABACiKgAUAoCgCFgCAoghYAACKImABACiKgAUAoCjbRMAed9xxafLkyfnnGTNmpN69e6fu3buncePGrXO/m2++Oe29995pjz32SD/5yU+aaW8BAGjRATtlypT029/+Nv+8dOnSdOKJJ6bhw4enWbNm5W3Tpk3L2+bPn5/OOOOMdPHFF+f7jxo1Kj388MPNvPcAALSogF22bFn63Oc+lw444IB8PYI1RlcjUvfff/8cqRMnTszbJkyYkAYNGpTOPPPMdPDBB6fzzz8/XXfddc25+wAAtLSAjXg95ZRT0oABA/L1efPm5Uitq6vL1/v375/mzJlT23b00UfXHlt/W2PWrFmTVq5cuc4FAIDyNVvAxtSAO++8M40dO7Z2W0Rmr169atc7duyYnnrqqdfc1pgxY8akTp061S49e/bcau8FAIDtPGBffPHFdPbZZ6fvf//7aZdddqnd3qZNm9SuXbva9fbt26fVq1e/5rbGjBw5Mq1YsaJ2Wbx48VZ7PwAANJ02qRl89atfTf369UsnnHDCOrd37do1H8hVtWrVqtS2bdvX3NaYiN36wQsAwPahWQL2hhtuyDHauXPnfD1GUm+66ab88xFHHFG739y5c1OPHj3yzxG8sTLBJz7xifW2AQDQcjTLFILf//73eVmsBx54IF9i6azLL788Pf7442nmzJlp6tSpae3atXl+7JAhQ/JjTj311PTTn/40PfTQQ+m5555L3/72t2vbAABoOZplBHbPPfdc5/rOO++cT1wQl/Hjx6fjjz8+3xYjtNUTHBxyyCHp05/+dDr00EPz/NdYZuu8885rjt0HAKAZ1VUqlUraxixatCgtXLgwDRw4MIdsfQsWLEhPPvlkOuqoozY6B7ahWMUgViOIA7piBQM20b+WNIOtbtv7VQRAE9vUXtsmA3ZrELCvk4ClqbSMX0UAbIFea/ZTyQIAwOYQsAAAFEXAAgBQFAELAEBRBCwAAEURsAAAFEXAAgBQFAELAEBRBCwAAEURsAAAFEXAAgBQFAELAEBRBCwAAEURsAAAFEXAAgBQFAELAEBRBCwAAEURsAAAFEXAAgBQFAELAEBRBCwAAEURsAAAFEXAAgBQFAELAEBRBCwAAEURsAAAFEXAAgBQFAELAEBRBCwAAEURsAAAFEXAAgBQFAELAEBRBCwAAEURsAAAFEXAAgBQFAELAEBRBCwAAEURsAAAFKVNc+8AADSlusvqmnsXaCEql1Saexe2W0ZgAQAoioAFAKAoAhYAgKIIWAAAiiJgAQAoioAFAKAoAhYAgKIIWAAAiiJgAQAoioAFAKAoAhYAgKIIWAAAiiJgAQAoioAFAKAoAhYAgKIIWAAAiiJgAQAoioAFAKAoAhYAgKIIWAAAiiJgAQAoioAFAKAoAhYAgKIIWAAAiiJgAQAoioAFAKAoAhYAgKIIWAAAitKsAfvPf/4z3XfffWn58uXNuRsAABSk2QL2Zz/7Wdpnn33SmWeemfbcc898PcyfPz/169cvdenSJY0YMSJVKpXaY2bMmJF69+6dunfvnsaNG9dcuw4AQEsL2BUrVqTzzjsv3X333emhhx5KV199dY7VNWvWpGHDhqW+ffum2bNnpwULFqTJkyfnxyxdujSdeOKJafjw4WnWrFlpypQpadq0ac2x+wAAtLSAXblyZfrWt76V+vTpk6+/613vSs8++2y6/fbbc9zG6Oq+++6bRo8enSZOnJjvE8G6xx57pIsvvjjtv//+adSoUbVtAAC0HJsdsC+99FI655xzNnqf73znO+mRRx7Z4PaePXumM844I/+8du3aNH78+HTKKaekefPmpQEDBqQOHTrkbRG4MQobYtugQYNSXV1dvt6/f/80Z86cDb5GjOZGKNe/AABQvjab+4Addtgh/fjHP05PPfVUHhF961vfmo444oh06KGHpjZt2qQ//OEPaeTIkTkwYxR1YyJKjz766NS2bdv05z//OX31q19NvXr1qm2PWG3dunU+yCsC9MADD6xt69ixY96HDRkzZky67LLLNvftAQCwvY3ARlTGAVYxhzUiddmyZelLX/pS2muvvdLnPve5dNJJJ6Uf/OAH6bDDDnvN54oR1t/97nd5SkAczBUB3K5du3Xu0759+7R69er1tlVv35CI6JiOUL0sXrx4c98qAAAlj8BOmjQpf/U/cODAHI/HHXdcbdsDDzyQ4/Waa67JB2CdfvrpmxzDcf8Y0Y3R2hg1jVUI6lu1alUeoe3atWs+kKvh7RsSsdswhgEAaEEjsLEqwGc+85nUqVOn9Mwzz+SDqGIea4RnjMZ+6EMfyre/5S1vSRdeeOFGnyuWw4pVB6oiRCNmY4msWGGgatGiRXkua8RrLK1Vf9vcuXNTjx49Nv8dAwDQMgI2lrqK0dH7778/nX322Tkmb7rppjzaGvNezzrrrLTjjjum733ve+nXv/71OrHZUMyb/eEPf5gv8dX+l7/85XTsscem448/Ps91jdHeEKsQDB48OM+DjSW0Zs6cmaZOnZoP/Bo7dmwaMmTIlvkUAADY/gI2pgjEPNXf/va3+aCq66+/Po/AxskIhg4dmk9E8OKLL+YpBrHU1ZNPPrnB53rzm9+cbr755nTVVVelgw46KM9lvfbaa/M81wkTJqTzzz8/n6zg1ltvTVdeeWV+TFyP1Qoicnfbbbf08MMPp6985Stb5lMAAKAYdZX6p7raiPgq/8Ybb8zLY/3973/P/42R2A984AOpW7duOVzjtLDHHHNMjts3YsmSJXmJrFhSK567vphWsHDhwvx6O++88yY/Z4zsxvSHOKArVjBgE/1r2TLY6jbtVxG8YXWX+b1G06hc4vfa5trUXtvkg7hOO+20fFDU008/nS8xX/UXv/hFjsx4oVtuuSWPyN51113pb3/7W9p7773T67X77runE044odFtscxW/aW2AABoWTZ5CsH73//+vERWrPn617/+NX/df/LJJ+eR0JhS8Pvf/z6PjH73u99N55577tbdawAAWqxNDth3v/vd+RSvMZwbIfv444/n5bSqZ8q66KKL8moBEbovvPDCRs+SBQAAr9cmTyG488478wkLYspsHEAVp4uN0ddYXitC9u67766dGSumEtxzzz15jVcAAGiWg7iqXnrppbyM1U477ZT+9Kc/5dO7xpJZMbUgxNM999xzaZdddknbEgdxvU4O4qKpOIiLJuIgLpqKg7i2Xq9t9qlk46QDEa8hlsCKExBU4zXEWbVizVYAANgaNjtgNyZO9RonJTj00EO35NMCAMCWD9g4GcEpp5ySl8C6/PLLt9TTAgDAlg/YOKjrPe95Tz4TV6wDG6eUBQCAbS5gn3nmmfT5z38+rzZw7LHHppkzZ6bOnTtvub0DAIDXs4xWnFkrTiPbvXv39Oqrr6Zly5bldV5nzJiRTyUbS2m97W1v25SnAgCArR+wixcvTjfddFM++1arVq3ywVoRtbvttlte6iBWJgAAgG0mYGN+a4yy1hdn24opA7Fs1jvf+c503nnnpVGjRpn/CgDAtjkHNkJ18ODB6brrrkv3339/PvPWUUcdlZYvX75l9xAAALb0KgQHHHBAmj59etprr73y6WVXrVq1JZ4WAAC23jqwrVu3zgd6devWLZ199tlb6mkBAGDrnYkrIvaHP/xhnlIQKxUAAECzHMS1udMJHnroIQdzAQCw7Y/AVolXAACKClgAANhaBCwAAEURsAAAFEXAAgBQFAELAEBRBCwAAEURsAAAFEXAAgBQFAELAEBRBCwAAEURsAAAFEXAAgBQFAELAEBRBCwAAEURsAAAFEXAAgBQFAELAEBRBCwAAEURsAAAFEXAAgBQFAELAEBRBCwAAEURsAAAFEXAAgBQFAELAEBRBCwAAEURsAAAFEXAAgBQFAELAEBRBCwAAEURsAAAFEXAAgBQFAELAEBRBCwAAEURsAAAFEXAAgBQFAELAEBRBCwAAEURsAAAFEXAAgBQFAELAEBRBCwAAEURsAAAFEXAAgBQFAELAEBRBCwAAEURsAAAFEXAAgBQFAELAEBRBCwAAEVptoC99dZb01ve8pbUpk2b9I53vCP9+c9/zrfPnz8/9evXL3Xp0iWNGDEiVSqV2mNmzJiRevfunbp3757GjRvXXLsOAEBLC9hHHnkkfexjH0tXXHFFevLJJ9Nb3/rWdOaZZ6Y1a9akYcOGpb59+6bZs2enBQsWpMmTJ+fHLF26NJ144olp+PDhadasWWnKlClp2rRpzbH7AAC0tICN0daI1w9+8INpt912S+eee26aO3duuv3229OKFSvy6Oq+++6bRo8enSZOnJgfE8G6xx57pIsvvjjtv//+adSoUbVtAAC0HG2a40WHDh26zvWHH344R+m8efPSgAEDUocOHfLtffr0yaOwIbYNGjQo1dXV5ev9+/dPX/rSlzb4GjGaG5eqlStXbqV3AwBAizqI66WXXkrf/OY30znnnJMjs1evXrVtEautW7dOy5cvX29bx44d01NPPbXB5x0zZkzq1KlT7dKzZ8+t/l4AAGgBAXvJJZeknXbaKc+BjQO62rVrt8729u3bp9WrV6+3rXr7howcOTJPR6heFi9evFXfBwAA2/EUgqq77rorXX311enee+9NO+ywQ+ratWtehaC+VatWpbZt2+ZtcSBXw9s3JGK3YQwDAFC+ZhuBXbRoUV5RIAL2wAMPzLfF8lmxwkD9+8Q81ojXhtvioK8ePXo0y74DANDCAvaFF17IB3KddNJJ6ZRTTknPPfdcvgwcODDPdZ00aVK+X6xCMHjw4DwPNpbQmjlzZpo6dWpau3ZtGjt2bBoyZEhz7D4AAM2orlL/TAFNeBKDk08+eb3bY8T1wQcfzCOzO+64Y2rVqlWaPn16bYT2mmuuSRdccEHaeeedU+fOnfOIbCzDtSkijONgrpgPGweAsYn+teoDbHVN/6uIFqruMr/XaBqVS/xe21yb2mvNErCvZcmSJWnOnDl5Sa1u3bqtF7kLFy7Mo7URsptKwL5OApamsu39KmI7JWBpKgJ2821qrzXrQVwbsvvuu6cTTjih0W2xlFb95bQAAGhZmn0ZLQAA2BwCFgCAoghYAACKImABACiKgAUAoCgCFgCAoghYAACKImABACiKgAUAoCgCFgCAoghYAACKImABACiKgAUAoCgCFgCAoghYAACKImABACiKgAUAoCgCFgCAoghYAACKImABACiKgAUAoCgCFgCAoghYAACKImABACiKgAUAoCgCFgCAoghYAACKImABACiKgAUAoCgCFgCAoghYAACKImABACiKgAUAoCgCFgCAoghYAACKImABACiKgAUAoCgCFgCAoghYAACKImABACiKgAUAoCgCFgCAoghYAACKImABACiKgAUAoCgCFgCAoghYAACKImABACiKgAUAoCgCFgCAoghYAACKImABACiKgAUAoCgCFgCAoghYAACKImABACiKgAUAoCgCFgCAoghYAACKImABACiKgAUAoCgCFgCAoghYAACKImABACiKgAUAoCgCFgCAoghYAACKImABAChKswbsM888k3r16pUee+yx2m3z589P/fr1S126dEkjRoxIlUqltm3GjBmpd+/eqXv37mncuHHNtNcAALTIgI14HTp06DrxumbNmjRs2LDUt2/fNHv27LRgwYI0efLkvG3p0qXpxBNPTMOHD0+zZs1KU6ZMSdOmTWuu3QcAoKUF7GmnnZZOP/30dW67/fbb04oVK/Lo6r777ptGjx6dJk6cmLdFsO6xxx7p4osvTvvvv38aNWpUbRsAAC1HswXsj370o3TBBResc9u8efPSgAEDUocOHfL1Pn365FHY6rZBgwalurq6fL1///5pzpw5G3z+GM1duXLlOhcAAMrXbAEbc18bisisf3vEauvWrdPy5cvX29axY8f01FNPbfD5x4wZkzp16lS79OzZcyu8CwAAWvQqBG3atEnt2rVb57b27dun1atXr7etevuGjBw5Mk9HqF4WL168VfcdAICm0SZtQ7p27ZpXIahv1apVqW3btnlbHMjV8PYNidhtGMMAAJRvmxqBjeWzYoWBqkWLFuW5rBGvDbfNnTs39ejRo5n2FACA5rJNBeyRRx6Z57pOmjQpX49VCAYPHpznwcYSWjNnzkxTp05Na9euTWPHjk1Dhgxp7l0GAKAlTyGIea4TJkzIa73GSQxatWqVpk+fnrfFyQvGjx+fjj/++LTzzjunzp0719aIBQCg5air1D/V1TZiyZIleYmsWFKrW7du62yLaQULFy5MAwcOzCG7qWJkN1YjiAO6YgUDNtG/li2DrW7b+1XEdqruMr/XaBqVS/xe21yb2mvb1Ahs1e67755OOOGERrfFUlqNLcEFAEDLsE3NgQUAgNciYAEAKIqABQCgKAIWAICiCFgAAIoiYAEAKIqABQCgKAIWAICiCFgAAIoiYAEAKIqABQCgKAIWAICiCFgAAIoiYAEAKIqABQCgKAIWAICiCFgAAIoiYAEAKIqABQCgKAIWAICiCFgAAIoiYAEAKIqABQCgKAIWAICiCFgAAIoiYAEAKIqABQCgKAIWAICiCFgAAIoiYAEAKIqABQCgKAIWAICiCFgAAIoiYAEAKIqABQCgKAIWAICiCFgAAIoiYAEAKIqABQCgKAIWAICiCFgAAIoiYAEAKIqABQCgKAIWAICiCFgAAIoiYAEAKIqABQCgKAIWAICiCFgAAIoiYAEAKIqABQCgKAIWAICiCFgAAIoiYAEAKIqABQCgKAIWAICiCFgAAIoiYAEAKIqABQCgKAIWAICiCFgAAIoiYAEAKIqABQCgKAIWAICiCFgAAIoiYAEAKIqABQCgKAIWAICiFBew8+fPT/369UtdunRJI0aMSJVKpbl3CQCAJlRUwK5ZsyYNGzYs9e3bN82ePTstWLAgTZ48ubl3CwCAJlRUwN5+++1pxYoVady4cWnfffdNo0ePThMnTmzu3QIAoAm1SQWZN29eGjBgQOrQoUO+3qdPnzwKu6HR2rhURfiGlStXNtHeApvF/zZpKi829w7QUmiO1/+ZvdYU0TalvalevXrVrtfV1aXWrVun5cuX5zmx9Y0ZMyZddtll6z1Hz549m2Rfgc3UqVNz7wHAFtXpCr/XXq9Vq1alThv5/4WiArZNmzapXbt269zWvn37tHr16vUCduTIkenCCy+sXX/11VfTsmXLUrdu3XL4wtb8h1b8Q2nx4sWpY8eOzb07AG+Y32s0lRh5jXjdY489Nnq/ogK2a9eueRWC+uJNtm3bdr37Rug2jN3OnTtv9X2Eqvgl7xc9sD3xe42msLGR1yIP4orls2bNmlW7vmjRojzPNcIWAICWoaiAPfLII/PXGJMmTcrXYxWCwYMH53mwAAC0DMXNgZ0wYUIaPnx4PolBq1at0vTp05t7t2AdMXXlkksuWW8KC0Cp/F5jW1NXKfBUVkuWLElz5szJS2rFQVkAALQcRQYsAAAtV1FzYAEAQMACAFAUAQsAbNQzzzyTz4T52GOPNfeuQCZgYQuKE23EesVxZrhYKcMUc2B7iNehQ4eKV7YpAha2kDipxrBhw1Lfvn3T7Nmz04IFC9LkyZObe7cA3pDTTjstnX766c29G7AOqxDAFnLLLbekj3/84+mJJ55IHTp0SPPmzUuf+tSn0j333NPcuwbwusVZL2P6QF1dXf55n332ae5dAiOwsKVEsMbaxBGvoU+fPnkUFqBkEa+wrRGwsIXEaY7r/6KP0Yo4zfHy5cubdb8AYHsjYGELnuq44WkW27dvn1avXt1s+wQA2yMBC1tI165d09KlS9e5bdWqValt27bNtk8AsD0SsLCFxPJZs2bNql2Pgx1iZYIIWwBgyxGwsIUceeSReR7spEmT8vXRo0enwYMH53mwAMCWYxkt2IJ++ctfpuHDh6cdd9wxtWrVKk2fPj0deOCBzb1bALBdEbCwhS1ZsiTNmTMnL6nVrVu35t4dANjuCFgAAIpiDiwAAEURsAAAFEXAAgBQFAELAEBRBCxQlGuvvTaddNJJ6aWXXmqS13v11Ve36PPFyS2+8Y1vpG3FK6+8kl5++eXUUv+u/OAHP0jLli1r0v0C3jgBCzSZRx99ND3++OO16//+7/+evvjFL75mYNVfLOWGG25IhxxySKOn6G0sNs8+++z0hS98oXb9hRdeSHvuuWeaNm1aevHFF19znz//+c+nD3/4w2lLOffcc9Pq1avf0HOsXbs2XzblfhuLt/i8+vfvn77//e+nlvp3pUOHDum0005b53HAtk/AAk3m29/+djr55JNrsdCuXbt80oeNOfPMM1P79u1Tp06dUseOHdMdd9yRrrrqqtS5c+faJbZFpIwZM6YWbs8//3ztNeoHTLze008/nbp3756OOeaY9OMf/7i2Lc6k9tRTT6VVq1al5557Ll8eeuihtN9++9WuxyW2P/nkkzmGN8ddd92V/va3v6VRo0bVbrvoootSly5dUp8+fdKDDz64Sc8zYsSI/Jj4POrq6mqfQ1yPE2jU/2zOOuusDT5P3PeUU05JV1xxxVYb0X7mmWdSr1690mOPPbZN/l2Jf5zEP2iuu+66N/AugSYX68ACbG1r166t9OzZs3LjjTdWXnjhhXwZPnx45aKLLqpdX7VqVWXZsmUbfI4vfOELlXPPPfc1X+vKK6+sDB06tPLqq69WPv3pT+fXqK99+/aVRx55pLJ48eJKr169Kn/961/z7RMmTKi0a9eu0qlTp3zZZZddop7yz61bt67stNNOtW3xHNOnT9+sz2DgwIGVuXPn1q5fc801lW7dulXuvvvuyq233lrp3bt3Zc2aNZv8fDNmzKgccMABteuLFi2q7Lbbbhu8/zPPPFN5/vnna593XB599NHKMcccU1m4cGHtttWrV1eWL19eee655ypvxNKlSyuHHXZY/gxj37bFvyvh6aefzp89UA4BCzSJa6+9thaB1UubNm0qO+ywQ+16xOOBBx643mMjRB944IFKly5dcnTG9Y156aWXKoMGDap885vfrAVsRNkrr7ySt3fo0KEWVP/85z9zEDXmsssuqxx99NH556997WuV//f//t/rfv9LliypHHzwwevcdsghh1TGjBlTu37yySdX7rjjjhxu1VCufzn88MPXefyIESPyZUMB+49//KP2c7zHVq1aVXbcccf859C2bdt1/iwaXiLQL7300sobEWF81VVXrRewJ510UqPv7zvf+U6T/12pOv744yuzZ89+Q+8XaDoCFtjqYqSsR48eOWbqO+OMMyqXXHLJJo3kRQSNHTu28pvf/CbHVYxcVi/VKHviiSfWibcYXawGbL9+/XLUxP2ro6rxc9euXSunnnrqeq95ww035BiMmAwxGrnffvtVrr/++tf1GUydOrXy8Y9/vHY9wiqC8r777qvdNnr06MoVV1yRXyuCr+HlySefrN037hP7v+eee1b23nvvHHcRftWAXbBgQWXXXXet3HvvvevtSwTk+PHjK1tbfP6hYcBGzDf2/lasWNEsf1fC5ZdfXpk4ceIWe+/A1tWm6SctAC3NP/7xj/SpT30qnXPOOXmuZWMH1YTGtsU80w996EN5vuZnPvOZtMMOO9Tmnsb94wCkuIwcOTK9+c1vrj3uTW96U77EnNODDjoo3X///fn2mLsacx7Hjx+fPvaxj623DzG/NeaoXn/99fko9njNOODru9/9bvrNb36TjjvuuDR16tR04YUXpoMPPnizPoPddtutdj3m0saBRDE/tCrmbf7lL39JO+20U75szGWXXZbe/va3p+nTp+eD0Xbdddc8jzbE3M94b7HawWGHHZaaS/33Vl/9z6Ghhx9+uMn/roT4/OLPCCiDg7iAre6AAw7I0fDHP/4xH4zTpk2bfImjxC+//PL8c0RHbKu/pNPf//73NGjQoHxgThys1Lp163WOvh83blz61a9+lebOnZs+8pGPrHdk/iWXXJJ+/etfr3Nb3DdeJyK0odtuuy1HV0Rk3O9973tfmjVrVj7gK/Zr3333TQ888EDq0aNHOuqoo9KPfvSjTf4MIkgjWqviPYfYl6p4n5uyQsGUKVPSpEmT0nnnnZffc+x3RHocoLR06dIcf7feeusWXT1he/+7En82O++8c5O+V+D1E7BAk4klmyIcIjzicvrpp+fRzvg5RiNjtKwadiGCIkbUfvGLX+TrER8RgnEEfkTl1772tXTPPfeknj175tHL2BajkfFccaR+HPX/iU98Yp19+PnPf57OP//8PJracBWBCNabb745B2GM0oauXbvm0bsYuQ3xGh//+MfTE088kT75yU9u8nvfe++90yOPPFK7HkfUxyWCs/7ob4wq3nTTTescOV+9HHHEEbXP5cYbb8xR9653vStdeumlOVrDLrvskuOvb9++aVsVqws09v5ilLup/65UxZ/NXnvt1cSfBPB6mUIANJkYOYvLhsToWX0RGZ/97Gdr1yPK6i/3FMETI6ITJkxYb4QyRl5nzpyZR+2qIhYjYOMxsc7oNddcs87zx7797Gc/y9MNPvjBD+bR2FgGKvYj1iCNbTGlM9YkPfXUU9OXv/zlTX7v8fX+woUL1xnpO/TQQ/MIb4zsVqMrRiBPOOGEvI8NVb8yj8X5w9FHH50D/be//W1eyzSWB6suI7Uti5MHNLYEWfxjoan/roT4M40R+bFjx76u9wM0PQELbHfOOOOMHHkRijFaV11LNOZFRpjus88+6atf/Wp697vfnUPnyCOPzNvjfrfffnsO22osxlfVMSdzyZIl+fp9992X58f269cvvec976k99rXE19rxlf43v/nNPLUhfOADH0hXXnllOvHEE/Pzx+jv3XffvUlzYENE+C9/+cs8naD+VIT64jVjzdPqiPJrnV0sPoM4W1jsb/U5I7pjPumGXmNzbWwObHOIuc7x52gKAZTDFAKgSVS/2n8tcZ+Gi+r/a8WUdW6LEwzEwv8NR+KqqjESzxVfO3/961/PBzzFf0PMGY2wi4OyYrH7cMstt+TXee9735vP0BRi6kCEW0wBiBG8iN7ly5fnExDESQ82R5wRLKYHzJ49u3aWsDh4KOIyDgj76Ec/uslf/cc83TgRQzzn0KFD820x7zNO4PDss8/m6IzR3RiNbjiSGaOfMVoZkdrwEveNqQ0R+FXx/E15tq6m/LsSB/nF34Pq3wugDHWxFEFz7wSw/Ys5jnGqz4isjYkgOfzww9Odd95Zuy1GBCM+Yk5kdd7j+9///nyq0W9961t5JHRD4iv2ONf94sWL88hqfG1f39VXX52P5o+DsmKOZERpjNJGHEXIxnzS6q/JeO1jjz02zz19vSKmIjpjpDfE68RUh4jkmPe5KSZPnpwDNFYZqD/HNz7fCOGI2/g5IjxGohvubwRsbN+Q2BbvtToKHF/5v/Od79zk/Svp70p8fjG/Nv4xAJRDwAI0sYjWjc3vfC2x3FMsNzVw4MDUFGJZroj57dEb/bMAmoeABQCgKP7ZCQBAUQQsAABFEbAAABRFwAIAUBQBCwBAUQQsAABFEbAAABRFwAIAUBQBCwBAKsn/B4pY1u2pMOaxAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 计算总体生存率\n",
    "total_survival_rate = df['survived'].mean()\n",
    "print(f\"总体生存率: {total_survival_rate:.2%}\")\n",
    "\n",
    "# 生存与死亡人数统计\n",
    "survival_counts = df['survived'].value_counts()\n",
    "print(\"\\n生存情况统计:\")\n",
    "print(survival_counts)\n",
    "\n",
    "# 可视化总体生存率\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "matplotlib.rcParams[\"font.family\"] = [\"SimHei\"]\n",
    "plt.figure(figsize=(8, 6))\n",
    "survival_counts.plot(kind='bar', color=['red', 'green'])\n",
    "plt.title('生存情况统计')\n",
    "plt.xlabel('生存状态 (0=死亡, 1=生存)')\n",
    "plt.ylabel('人数')\n",
    "plt.xticks(rotation=0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "81354cfa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "按性别分组的生存率:\n",
      "        总人数       生存率\n",
      "sex                  \n",
      "female  314  0.742038\n",
      "male    577  0.188908\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "性别与生存情况交叉表:\n",
      "survived    0    1  All\n",
      "sex                    \n",
      "female     81  233  314\n",
      "male      468  109  577\n",
      "All       549  342  891\n"
     ]
    }
   ],
   "source": [
    "# 按性别分组的生存率\n",
    "survival_by_sex = df.groupby('sex')['survived'].agg(['count', 'mean'])\n",
    "survival_by_sex.columns = ['总人数', '生存率']\n",
    "print(\"按性别分组的生存率:\")\n",
    "print(survival_by_sex)\n",
    "\n",
    "# 可视化性别与生存率关系\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.barplot(data=df, x='sex', y='survived')\n",
    "plt.title('不同性别的生存率')\n",
    "plt.ylabel('生存率')\n",
    "plt.show()\n",
    "\n",
    "# 交叉表分析\n",
    "sex_survival_crosstab = pd.crosstab(df['sex'], df['survived'], margins=True)  # 功能比pivot_table() 更加简单，传入横竖两列，返回交叉表\n",
    "print(\"\\n性别与生存情况交叉表:\")\n",
    "print(sex_survival_crosstab)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "e5b89773",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "按舱位等级分组的生存率:\n",
      "        总人数       生存率\n",
      "pclass               \n",
      "1       216  0.629630\n",
      "2       184  0.472826\n",
      "3       491  0.242363\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "舱位等级与生存情况交叉表:\n",
      "survived    0    1  All\n",
      "pclass                 \n",
      "1          80  136  216\n",
      "2          97   87  184\n",
      "3         372  119  491\n",
      "All       549  342  891\n"
     ]
    }
   ],
   "source": [
    "# 按舱位等级分组的生存率\n",
    "survival_by_class = df.groupby('pclass')['survived'].agg(['count', 'mean'])\n",
    "survival_by_class.columns = ['总人数', '生存率']\n",
    "survival_by_class = survival_by_class.sort_index()\n",
    "print(\"按舱位等级分组的生存率:\")\n",
    "print(survival_by_class)\n",
    "\n",
    "# 可视化舱位等级与生存率关系\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.barplot(data=df, x='pclass', y='survived', order=['1', '2', '3'])\n",
    "plt.title('不同舱位等级的生存率')\n",
    "plt.ylabel('生存率')\n",
    "plt.xlabel('舱位等级')\n",
    "plt.show()\n",
    "\n",
    "# 舱位等级与生存情况交叉表\n",
    "class_survival_crosstab = pd.crosstab(df['pclass'], df['survived'], margins=True)\n",
    "print(\"\\n舱位等级与生存情况交叉表:\")\n",
    "print(class_survival_crosstab)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d1959cd7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "年龄分布统计:\n",
      "count    891.000000\n",
      "mean      29.361582\n",
      "std       13.019697\n",
      "min        0.420000\n",
      "25%       22.000000\n",
      "50%       28.000000\n",
      "75%       35.000000\n",
      "max       80.000000\n",
      "Name: age, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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AAOAXSEoVI20uv3///iI/PykpSWrWrFmsZQIAAMGvffv2LusbNmyQunXryooVKyQlJcUkpFSDBg1k7dq1PiolAACAK5JSxZiQOr9ePTmalVXkc5SOjZX169aFbGLqTJJ6DJQNAMDfTpw4IS+++KIMGDDAjCOVnJzs2KdjSUVERMjBgwclISEh13OPHz9uFltGRobXyg0AAEIPSalioskUTUgVdUwd7b40/ck+5jyhmJQqjqQeAAAQGTp0qJQpU8aMKfXkk09KdHS0y/6YmBjJysrymJQaMWKEDBs2zC9mDVOzujBmDUJcXuM5KcZ0AhAESEr5yZg6oe5Mk3oMlA1fo/suAH+gA5uPHTtWlixZIqVKlZLExEQz+56zzMxMiYqK8vj8QYMGmRZWzi2latSoUeLlBoA8MdA6ENRISsGvMFA2AhHddwH4gy1btkiXLl1MUqp+/fpmW5MmTeSNN95wOUa752myyhNtVeXesgoAAKCkkJQCgDNE910Avnb06FEz2HnHjh2lU6dOcvjwYbO9RYsWprXT5MmTzYx7Ovte69atzbhSAAAAvkZSCgCKCd13AfjKV199ZWbV08W9ZdTEiRNNC6q0tDQJDw+XBQsW+LSsAAAANpJSAAAAAU5bSFmW5XFfrVq1ZNOmTbJs2TJJSUmRChUqeL18CG75Dlbv1ZIAAAINSSkAAIAgV7lyZWnXrp2viwEAAOCCpBQAAAAQSrOWKWYugxNauwHwlXCfvTIAAAAAAABCFi2lAOAf69at8+rzivM8SUlJzNwHACgSWskAAHyFpBSAkJe5f4+EhYfL7bffHrCvXzo2VtavW0diCgAAAEDAICkFIOQdzcwQKydHbn12vFRMrlvo529YNE/mjhvhs9ffu+V3mf5kH9m/fz9JKQAAgIKMrca4aoBfICkFAP/QhFC1ehcXKSnky9cHAIS2vLrf8Sd34Ajqz5CkEIB8MNA5AAAAAAAAvI6WUgAAAAACWlC3NAKAIEZSCgCCBLP3AQAAAAgkJKUAIMAxex+AUJZXCxk1q8ss/xxLRzGeDgAAJKUAIND5w+x927dvN88vKlpqAQAAAKGHpJSf8WX3G/6oBALbmc7eV9TfP7t27ZKbb7lFjh09WuTXpqUWAI+YtQsAgKBGUspP+Lr7jSakzq9XT45mZfnk9QEE9u8f5cuWWgAAAAACD0kpP+Hr7jf6PE1I8UclEHrO9PfPhkXzZO64EWfcUgsAAISmfMeG83EZGJsOKFkkpfyMr/+o8/XrA5CA+/+vSWkAAAAAKCySUgAAAACAgG7p5Ldo6QTki6RUkCnqQMVnMsA6AAAAAPhSnt3vzvjEJJWYdAIliaRUkCiugYoBIBAxeygAAAAQeEhKBYniGqgYAAJNccweGh0TIx/+3/9JlSpVivR8kloAAIS2M22pVWItvQA/R1IqyDBQMYBQc6azh25Z/qPMHjVY2rdvX+QylI6NlfXr1pGYAgAAAAqBpBQAICjGxDuTpPyZtDTV509/so9JjpGUAgJTni0U3svnSYylAgChgXHFShRJKQCAT/nLmHhFTWoBAAAAKBqSUgAAn2JMvDPHQO8AAMCXrXzyanGab6vTgr4+s/8FNZJSAAC/EOhj4hW1++GZJoWKY6B3xsQCAACAL5CUgl+NCQMAodj98EySQmc60DtjYiGoeal2vcRm3epCKwAABcPsfQhUAZeUWr16tXTv3l02btwoPXv2lJEjR0pYWJivixXy/GVMGAAItO6HxZUUYkws5If4CQAA+KOASkodP35cUlNT5brrrpP3339f+vfvL1OmTDFBFnyLMWEAhDqSQvBXxE8AgDMaE8qrJUGoCaik1Jw5cyQ9PV1GjRolsbGxMnz4cOnbty9BlR8J9DFhACBU+WpMLMVA7SWL+AkAgBAfbL6L/6YWAyoptWLFCklJSTEBlWrQoIGsXbs2z1pBXWwajKmMjIwSKdvhw4fN45/rVsqJrCOFfv6+rX8nZXg+z+f5PJ/nh9jzt20yj8uWLXPcSwpjw4YNZ/T6W1cuFQkLO6Pu19ExMTL17belUqVKhX7unj175I4775Tjx44V+fVjSpeWpT//LDVq1JCSYMcOlmVJIPLn+EmdzDqZ576MvHY5ledMn5/fOfJ8fgHLcMbPL+D7XlKvn+85/Oj5+Z0j4L8DPn5+vufwo+fndw6+A2f2fG9+hnk6mccJSvDeVKDXL2gZzvT5BZTvZ+Ct96oI8VOYFUAR1sCBA+XYsWMyduxYx7azzjpLfvvtN0lISHA59qmnnpJhw4b5oJQAACAY7dixQ6pXry6BhvgJAAD4a/wUUC2lIiMjJTo62mVbTEyMZGVl5QqqBg0aJAMGDHCs5+TkyIEDB6RChQrFNrCnZv60Vlbf5Li4OAkFoXbNoXa9oXjNXG/wC7Vr5nqLn9bfZWZmStWqVSUQ+VP8FGrfT8U1c83BKNSuV3HNXHOwyiihay5o/BRQSanExEQze4wzvcioqKhcx2rw5R6AlS9fvkTKpR9cqHxhQ/WaQ+16Q/Gaud7gF2rXzPUWr/j4eAlU/hg/hdr3U3HNoSHUrjnUrldxzaGBa/Ze/BQuAaRJkyayePFix/qWLVvMuAcabAEAACA34icAAOCvAiop1bJlS9O0bPLkyWZdZ49p3bq1RERE+LpoAAAAfon4CQAA+KvIQBsTYeLEidKlSxdJS0uT8PBwWbBggc/Ko83bhw4dmquZezALtWsOtesNxWvmeoNfqF0z1wt/jp9C8fPimkNDqF1zqF2v4ppDA9fsfQE1+55t9+7dZupsnd5YB94EAABA/oifAACAvwnIpBQAAAAAAAACW0CNKQUAAAAAAIDgQFIKCHGHDh2SH3/8UQ4ePOjrogAAAAQE4icAKB4kpYpo9erVZorlhIQEM2hosPaC3L9/vyQnJ8vWrVtD4tpnzpwptWvXNoPCNmzYUNatWxfU1zxjxgypVauW9OzZU6pXr27Wg/l6nbVp00amTJlifl64cKHUq1dPkpKSZNSoURJM+vfvL2FhYY7lnHPOCYnP+LHHHpPU1FTHerBer36HnT9fe9Htwfq91gG7a9SoIbGxsXLVVVfJ5s2bg/ozDiah8hkROwV37BTq8VOoxFChGj/ZiKOIoyxvfsY6phQK59ixY1atWrWs++67z9q4caPVtm1b680337SCzb59+6zLLrtMv43Wli1bgv7a9XoSEhKsDz74wNq9e7d1yy23WJdffnnQXvOhQ4espKQka8WKFWZ98uTJ1tlnnx201+vsnXfeMd9rvea9e/dacXFx1rBhw6zffvvNuuSSS6xvvvnGChbNmjWzPv/8c+vgwYNmycjICPrPWL/TZcuWtTZt2mTWg/l6jx8/7vhsddmxY4f5f71kyZKg/F7r51ejRg1r2bJl1rZt26wePXpYLVq0COrPOFiEymdE7BTcsVOox0+hFEOFYvxkI44ijmrr5c+YpFQRfPzxx+YGfOTIEbP+66+/Ws2bN7eCzb/+9S/rpZdecgmsgvnaZ82aZb322muOdf3FU7p06aC95u3bt5vAwv0GFKzXa/vrr7+sSpUqWeedd54JqEaPHm2df/75Vk5Ojtn/ySefWN26dbOCwcmTJ80NNTMz02V7MH/G2dnZ5g/CwYMHh8T1uvvPf/5j3XvvvUH7vZ4xY4b5o9f2/fffW1WqVAmpzzhQhcpnROwU3LFTKMdPoRRDhWL8ZCOOIo7yxWdM970iWLFihZlOWZu8qQYNGsjatWsl2Lzxxhum6WqoXHv79u2lV69ejvUNGzZI3bp1g/aatdlmt27dzM8nT56U0aNHS6dOnYL2em0DBw4016nXqPR6W7VqZZrqqqZNm5op04PBqlWrJCcnx3SnKF26tGluv3379qD+jCdMmGCuW7tVfPrpp3LixImgvl5nx44dk5deekn+/e9/B+33un79+vLNN9/Ir7/+Kunp6TJu3Di55pprQuYzDmSh8hkROwV37BTK8VMoxVChGD/ZiKOIo3zxGZOUKoKMjAwzVoBNv6wRERFBN9Ch8zWG2rXrL+AXX3xRevfuHfTXrL+EKleuLF988YW8/PLLQX298+fPl3nz5snIkSMd29yvNy4uTnbu3CnBQG8m5513nkydOlVWrlxpxvvQPx6C9TM+fPiwDB061Ixtsm3bNvOHwhVXXBG01+vuvffek8suu8wEksH6vdZg6uabb5ZGjRpJ+fLlZfHixfLCCy+EzGccyELlMyJ2Co3YKdTip1CLoUItfrIRRxFH+eozJilVBPqLKTo62mVbTEyMZGVlSbALlWvXX8hlypQxA1gG+zVrJvyrr74yNZvBfL1a+3HffffJ+PHjpVy5co7t7tcbDNdq05rcpUuXSrNmzcznq7Uhc+fONbV/wfgZf/TRR3LkyBETOA8bNsxca2Zmprz55ptBeb2eajf1j8Fg/l7/9NNPMmvWLFmyZImZ+apLly7Stm3boP29FUxC+TMKlWsPpdgplOKnUIyhQi1+shFHEUc58+Y1k5QqgsTERNm3b5/LNv0PGxUVJcEuFK5dmzSOHTvWZMtLlSoV9NesmfDGjRvLW2+9ZW5GwXq9zzzzjJlRol27di7b3a83GK41LxUrVjQBldbsBuNn/Mcff5imxzpLitIbrP7RoDfdYLxeZxs3bjSLNsEO5u/1tGnTpHPnzqYmMz4+Xp599lnZtGlT0P7eCiah/BmFwrWHWuwUSvGTCvUYKtjjJxtxFHGUM29eM0mpItBfytrUzbZlyxY5fvy4+TCDXbBfu16PZow1sNLmjcF8zTrNqU73adNfOhpg6dSnwXi9GijrtNXaVFUXXb///vtNMOl8vcuXL5dq1apJMNDPV6/TptcZHh4uF110UVB+xjot99GjR122afPzMWPGBOX1Ops+fboZ20X/GPT0eytYvtf6R8HevXtdAiatxdPAOdg/40AXrPfSggj2aw+l2CkU46dQjKFCLX6yEUcRR/nsM/bakOpBRGdkOOussxzTJPbs2dN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",
      "text/plain": [
       "<Figure size 1200x800 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 年龄的基本统计信息\n",
    "print(\"年龄分布统计:\")\n",
    "print(df['age'].describe())\n",
    "\n",
    "# 年龄分布直方图\n",
    "plt.figure(figsize=(12, 8))\n",
    "plt.subplot(2, 2, 1)  # 创建子图\n",
    "plt.hist(df['age'], bins=30, color='skyblue', edgecolor='black')\n",
    "plt.title('年龄分布直方图')\n",
    "plt.xlabel('年龄')\n",
    "plt.ylabel('频数')\n",
    "\n",
    "# 按生存状态分组的年龄分布\n",
    "plt.subplot(2, 2, 2)\n",
    "survived_ages = df[df['survived'] == 1]['age']\n",
    "died_ages = df[df['survived'] == 0]['age']\n",
    "plt.hist([survived_ages, died_ages], bins=30, alpha=0.7, \n",
    "         label=['生存', '死亡'], color=['green', 'red'])\n",
    "plt.title('按生存状态分组的年龄分布')\n",
    "plt.xlabel('年龄')\n",
    "plt.ylabel('频数')\n",
    "plt.legend()\n",
    "\n",
    "# 箱线图：年龄与生存率关系\n",
    "plt.subplot(2, 2, 3)\n",
    "sns.boxplot(data=df, x='survived', y='age')\n",
    "plt.title('生存状态与年龄分布箱线图')\n",
    "plt.xlabel('生存状态')\n",
    "plt.ylabel('年龄')\n",
    "\n",
    "# 小提琴图：年龄分布密度\n",
    "plt.subplot(2, 2, 4)\n",
    "sns.violinplot(data=df, x='survived', y='age')\n",
    "plt.title('生存状态与年龄分布小提琴图')\n",
    "plt.xlabel('生存状态')\n",
    "plt.ylabel('年龄')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "3a310fd9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "按性别和舱位等级分组的生存率分析:\n",
      "                人数       生存率\n",
      "sex    pclass               \n",
      "female 1        94  0.968085\n",
      "       2        76  0.921053\n",
      "       3       144  0.500000\n",
      "male   1       122  0.368852\n",
      "       2       108  0.157407\n",
      "       3       347  0.135447\n"
     ]
    }
   ],
   "source": [
    "# 性别和舱位等级的交叉分析\n",
    "plt.figure(figsize=(12, 5))\n",
    "\n",
    "# 性别和舱位等级对生存率的影响\n",
    "plt.subplot(1, 2, 1)\n",
    "sns.lineplot(data=df, x='pclass', y='survived', hue='sex')\n",
    "plt.title('不同性别和舱位等级的生存率')\n",
    "plt.ylabel('生存率')\n",
    "\n",
    "# 年龄、性别和生存状态的散点图\n",
    "plt.subplot(1, 2, 2)\n",
    "sns.scatterplot(data=df, x='age', y='fare', hue='survived', style='sex', alpha=0.7)\n",
    "plt.title('年龄、票价与生存状态关系')\n",
    "plt.xlabel('年龄')\n",
    "plt.ylabel('票价')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# 详细交叉分析表\n",
    "detailed_analysis = df.groupby(['sex', 'pclass'])['survived'].agg(['count', 'mean'])\n",
    "detailed_analysis.columns = ['人数', '生存率']\n",
    "print(\"按性别和舱位等级分组的生存率分析:\")\n",
    "print(detailed_analysis)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5a35b4c8",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "e:\\Documents\\git-notebook\\.venv\\lib\\site-packages\\seaborn\\utils.py:61: UserWarning: Glyph 8722 (\\N{MINUS SIGN}) missing from font(s) SimHei.\n",
      "  fig.canvas.draw()\n",
      "e:\\Documents\\git-notebook\\.venv\\lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 8722 (\\N{MINUS SIGN}) missing from font(s) SimHei.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n"
     ]
    },
    {
     "data": {
      "image/png": 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Ee4KCglIti5itVqSSiDQakZYkPkfktot8/NSGwkwgWjLEe0RrQMLPExVw8WQ+aRlE2Y8fP57i+IQ8f1HBTVrJFz8zYZ6BpItIV0r6PtHnQuTki1GZxPUSi0jvSegTII4RE4yJ1g1RSRfBlxhGVEyyllpQlfB/8XRepCWJayeWhHkVxGsRmInrKK6RIK7t3LlzZSAi+kWIJ/4imBJP5gXRYiACA5GOlTBHhDg3ERQkJYYCFddNpIyJ9KmE0aME8bPF3BTi34MI3ERwl7wfScJEdQnXXJRR/LsSx4iO2mLUKtGfIGG/KH9agRUREWUhBRHpPA8PD0WrVq0UZmZmijFjxigiIiLk9mvXrinq1KmjsLKyUmzevFluCwoKUri5ucn3Hj9+XPkZq1atUpiYmCjmzZunyJs3r8Lc3FxhaWmZ6mJhYaEwNTVVjBs3Th57+vRpxfTp0xW9evWSn62np6eoVKmSYvHixYrIyEjFjh07FA0aNJDbxc8oV66comXLloqlS5cqf744rmDBggpHR0eVxd7eXlGgQIEU24sXLy7Lsnfv3hTXQ+z/6aeflOv9+/dXtGjRQuHr66uyzJo1S2Fra6t83+3bt+VnNmrUSJ5TegIDAxUjR45UlCxZUvH06VOVfQ8fPhRRgMLT01Oui9cZLQsXLpTv/fHHHxVFihSR1y1Bx44dFX379pWvP3z4oChRooRi8uTJyv1du3ZVtG7dWrk+fvx4+ZniHojzTE2zZs3ke8T5fvvtt4q4uDiV/Z9//nmmyp106dy5c7rXjIiI1E9P/Ccrgw0iyvnEiD8iZUaMGCTGg0+Q0HdA9BdImhYkOq2K0WBELrk6iFGKRD8EMfa/6FQq0nWST3oliKFHxVNqMcylSDERI/SkNwLOpxKtEqIFJOGJvnjyLX5VZmbUG9EykloKVVpSG99ftJqIDtVi2FAxwpJ4Oi9StFIbySdhRCIxypBIoRIpP+I6idz8BOJaiZafhKf7Ik1ItBgk/Fzx1F4MDZqQwy8+U3R2FsOUpvYzBdG6It4nOiinNr+AaBURw9WKoWozQ/zbE60cCelMRESUPRgMEBGR2okAQ/TdSD4XBBER5SwMBoiIiIiIdBQ7EBMRERERqZlIMRWplAkDNmTk5MmTcpZ4MYy1mNcnuzAYICIiIiJScyDQunXrTAcCYsS4tm3byhnlxQzzYvjp1Ea9ywoMBoiIiIiI1EjMpyImzcwsUfkXw2mPGzdOThg5fvz4VCftzAoMBoiIiIiIMiBGbhNzoiRdxLa0Jn8cMWIEMuvmzZsqk1BWr15djrSXHTgDMRERERFphb1Gzhr72Zd/6S5nuk9KTKQoJlFMTvQV+BgisBBDSiewsLBAcHAwdCYY0OSNpdS1ivXClvNxmi4GpaJLLX3cfRSi6WJQKtxKF4and6Cmi0HJuDja44nndU0Xg1Jh51IJEae3aroYlArTup01XYQcZ+zYsRg5cqTKNjEHjDqIGeOTfpYYmjkiIkItn53hz86Wn0JEREREpMVMTEzUVvlPLn/+/LITcdJJP8VEktmBfQaIiIiIiDSoWrVqchShBNevX0fRokWz5WczGCAiIiIiygaib0BsbGyK7WJY0bNnz+LIkSNy/8yZM9GsWbPsKBLThIiIiIhIO+gZxY+2o63c3d0xb948tG/fXmW7mGhs7ty5aNmyJczMzGBlZYU1a9ZkS5kYDBARERERZQGFQqGynt4kZIMGDZKtAZ6enqhbt64MCrIDgwEiIiIi0gr6htrdMpARMSTpxw5L+v9inwEiIiIiIh3FYICIiIiISEcxTYiIiIiItIKeEZ9jqxuvKBERERGRjmIwQERERESkoxgMEBERERHpKAYDREREREQ6ih2IiYiIiEgr5PZ5BjSBLQNERERERDqKLQNEREREpBX0jNgyoG5sGSAiIiIi0lEMBoiIiIiIdBTThIiIiIhIK7ADsfqxZYCIiIiISEexZYCIiIiItAI7EKsfWwaIiIiIiHQUgwEiIiIiIh3FYICIiIiISEcxGCAiIiIi0lGZ7kDcsGFD6Oll3Gnj2LFj/2+ZiIiIiIhS4NCiGgwG+vTpo3x9/fp1rF+/Hv3794ejoyP8/f2xfPlytG/fPguKSEREREREGg0GevfurXw9bdo07NmzBzVr1lRua9OmDfr27av+EhIRERERiaFFDdgykCP6DLx69QofPnxQ2RYXF4cXL16oq1xERERERJQTJx0bNmwY2rVrhy+//BLFixdHcHAwPDw8MHDgQPWXkIiIiIiIck4wMG7cOFSoUAH//vsvjh49CltbW/z555/o1KmT+ktIRERERCRSWpgmlDOCAaFt27ZyISIiIiIiHeozEBsbi99//112IC5atCju3r2L6tWrw9vbW/0lJCIiIiKinBMMDBkyBFu2bJGjB7179w6mpqb47LPP2GeAiIiIiCi3pwlt27YNV65ckXMM/PTTTzAwMMCYMWNQpkwZ9ZeQiIiIiEgMLarPPgM5omWgWLFiOHXqlHJdzEwsUoUcHBzUWTYiIiIiIsppLQMzZ86Usw3/9ddfiIiIwMiRI3H69GmsXbtW/SUkIiIiIqKcEww0b94cd+7ckf0GKlWqBHt7e8yYMQOlSpVSfwmJiIiIiOQMxJ+U1ELqDgbEbMOlS5fGzz///CmHExERERGRtgYDYpIxkSYkJhn7/PPPYWj4ydMVEBERERFlCicdU79Pams5efIkXFxcMHv2bJki1KtXL+zatQvR0dHqLyEREREREeWcYMDNzQ2jRo3CkSNH4OPjgy5dumDjxo0oWLCg+ktIRERERERZ4v/K77l69Sr279+Pffv24c2bNxg+fLj6SkZERERElATnGcghwUDPnj1x+PBh2XdA9BsQQ4yWK1dO/aUjykKvXwQj/O1LFCpWBoaGxpouDpHWCQkOQmRkBEqULCUnnyQiIh0JBlxdXTFu3DjOOJyEkY016pzfhguf90Lk4yBNF0dnhAY+wI6Vv+BFqD+q1PsCzbqOkpPgZWT/xum4cXYn8uazREx0JPr+uBoFi6gOjRsZ/hYLfm6Fb37dBOuCRbPwLHKHx34+WDRvBp4EB6FJs1bo1W9Qpu7FuTMnsGbFEnz48B59+g9B3QaNVfZHR0XhuyF90av/INSqXV9uUygU6Nm1NSLCw5Xv696zHzp365UFZ6b9Hvv5YsHcWQgJCcLnzVqiT79vMnVvzp45idXLl+L9hw/oN2AQ6jVopBxRbtb0ybh/7y4MDQxgkicPps6YAysra3lvenRph/Ak96ZHz77o0v2rLD1HbeLzOADTFyxBUEgoWn/eEIP69Mjwfpw4ewF/rl6P9+8/YEi/r9CkXm25PTo6BlPnLcbl67dgZGSI5o3qYVDvHtDX18eHD3GY99cqHDl5Fh8+fECT+nUwcnB/ec8oY4+CQjFh9T8IePoCHepWxXdfNMvwPi3bdQwbjpxHZEwM6pQvg8n9v0C+PCbK/eK703fGcjSu7IZezepkw1kQZVGfgbFjxzIQSBYIVNu5FKYO9pouik55HxuD9fOGoEgJNwz+bSueBT/C9TM7MjzO9/4leN04ge9nHcZ3Mw6gdLnaOL13eYr3Hdw8C2FvnmdR6XOX2NgYTJv0MxxLl8Gs+csQ4P8Yx44cyFQAMW/WVHTu3hPjJ8/CJo9VCAr0V3nP5g1rYFekqDIQEEKCA5EvnxnWbd6tXNp36pYl55Yb7s2Uib/CsbQTZs9fIu/N0cMHMxVAzJk5TVbif5s8HRvWr0FgYIDcd/zoIbx+9RIr1mzAX6s9kD+/Dfbv2SX3BQfF3xuPLTuVS4cvumT5eWqLmNhYjJ0yE86OpfDX7KnwCwjC/qMnMwwepsxZhF5dOmLWb2OxasNW+AcGy30bd+yGoaEB1i6ejRnjf8LJc5ew/1j8523YvhOPfPywZOZkLJ4xCWcvXcH+oyey5Ty1XUzse3y7cB3KligCj18Hwyf4KXadvZbuMfsu3MC+izex+Lve2DZxBHxDnmH1vlMq79l28jLCIqPRvXGtLD4DoszjzA1qUNljDoI37dF0MXTOg1unEB0ZhubdxyC/bXE0+eJ7XD21PcPjDIyM0K7vJOTJaybXC5coi4iw1yrv8fO6DM8bx2FqZpVl5c9Nrl25KJ/S9x0wFHaFi6JH7wE4emhvhscdObQX5dwr4vNmrWWqSYvWHXDy2CHlfl+fR9i/518MGDRC5biHDzzh7OKGfGbmysXIiKleqbl6+ZK8N/2/HozChYugZ+/+OHJof4bHHT64D+UrVETT5q1Q0qEUWrVuhxNHD8t9ogXg60HD5bDS4gm02P/u3Vu57+FDLziXdYWZmZly4b1JdPHqDYRHRGBo/54oWtgOX/fshn1Hjqd7zN7Dx1CpvBtaN20Ex5LF0bFVUxw6cVru83z4CE3r10VBm/wo6+SIKhXKIyjkidz35u1bjPthOEoWt4dTqZKoUbkiHvr4Zct5aruzdx4gLCIaP3RpgWK2NhjW8XP8e+Zqusc8efkGk/p1QrlS9iheyAZNq5WHp3+Icv/T12+x8J/DGNO9FYwM2Trz/wwtqqklt2IwoAa3Bo2D36J1mi6GznkS4AV7R3cYm+SV63bFnPEs2DvD44qXrgQHl+rydfi7V7h26h+UrdJEpcVh55rf0KrHzzA2Mc3CM8g9/Hy9UcbFVaaLCCUdHBHo/zjD4x77eKN8hcrKdacyZeH96IF8LdJNli6cDeeybvC6f1cGBgkePfCUAcFXXVqhz5ftsWHtCvl+SsnP1wfOLmWT3JtSsnUgI76+3nB3r6hcd3J2Ud6bKtVqoJRjafk6NPQJzp45hRq14tNWHnp5yuXLzm3Rq3snrP97Fe9NEt5+j+Hq7IQ8JvGpI6Jy7xcQmO4xj3wfo5K7m3Ldxak0vLx95GtR0d9z+BjehYXD1z8AF65cR9UK7nLfsAG9UcSukPK4gKBg2Be2y6Izy10eBDxB+VLFkNckPpAtY28Hn+Bn6R7Tr2V9VHAsrlz3e/JcBgUJ/ti0D4VtrGTQcOORagsokVb0GShVqhRu3boln/I4ODikmTcnhhrVNZF+6f8ip/+Px/xh8PO8lGK7nr4+ytdombiupye3RYa/kX0BMnLlxBbs2zANJcpURZV6nZTbT+5ZhgJ2JeVnH9oyW41nov2mT/4Fd27fSLFdX98AderF55Mn3AvxxDjs3TuYmZun+XkREREoVKiwcj2vqSlevnwhX589dQwPH9xH3QZNEBwUAI+1K9CmfWeZDiTWq9aohdZtv8CTkCDMmTkJxUs4oE591f4GuuT3SeNw5/bNFNvFfahTr+FH35tIcW/sEu+NqWk+5b1J4LFuNf7ZugmNP2+OChUrK9OEqtWohTbtOiIkJBh/zJiC4iVLol79xH8fuiw8IhKFbW1T3I93YWEwN4tvrUwuIjIShQslHpPPNC9evHwlX/fo1A69ho1C6x795XqHlk1ROUngkODarbsyWJjW4McsOKvcR6TyFC1onew+6eFteCQs8sU/gErP4yfPcfz6PWwcN0Su3/T2x+Erd2Q/gsBnL7Fi70l85lYaP/Vok6XnQaTWYGD16tUwNY1/SrpmzRp8CjEpWfKJyUz+ezpClJZ2fX5DbEzKCe3OH16bIig1NDJBbExUpoKBirXbw9yqIHatnYQLRzxQs0kPPA32xuXjmzFk4j9qPYfcYtCwHxCTyr3Ys3N7inthZGyM6OiodCucYgQaQyMj5bqxsTFioqPk60MH9uCzOg3w/ehf5XqV6rUwYexINGvRFuMmzVQeIyqsrdp0wrmzJ3U6GBgy/HtEx8Sk2L575z9I/ugmM/dGBHhJ7038Mar3vuMX3WBvXxzLlixA1eo1UL3GZ5gwebrKvWndtgPOnTnFYOA/Bgb6sqNvUsZGRoiKjoF56rEADPT1YWxomOL9wkqPLShf1hnfD+wnWwemzF2E7XsOoFPr5sr3R0ZF4Y/Ff6F31y9gZWmRVaeWqxga6EMB1VQeEyNDRMXEZhgMiE7Cv635R3Y6diwa3zKz49QVlC9ljwUjesrflR3rVUXLMbPRrXFNlLTjHE0fQy8Xp+vk+GCgfv36qb7+GNOmTcPEiRNVtk2YMAHVPunTSFeYWRZIc/vTwIcq22Iiw2FgkFiBSY+hkTGcKzZE43evcP7wetRo/CV2rZmAJh1HwMI68SkcJbKyzp/m9oDHvirbxJCTSSuTqRGV0bdvXqs8jTY0jD/mxfNnaNgksUJTyrEM3r+PxfPnT1GseEmVz7G0ssLLF7rd2Tute2NtbQ1/P9U88ahM3BvzVO+N6p+MvHnzon7DxggKCsCRQwdkMJCcpaW1zt+bpCzMzODjH98RO2ll3SjZtU3K3NwMr9/G98kQIiIT33/45Fks+H0CrK0s5SI6Ga/euFUlGJi3bDVsCxZA1/atsuScciNR4fcOClXZFh4VIztrZ2T5nhN4Ex4pRx9KEPrqDWqXL6N8aGKX3wrWZqYIePqSwQDpVp8BMQqRmJws6SK2EX0Ke4fyCPBOTIt49SwQ79/HIK9Z+q0C5w+txc3ziR2+RfAgmunfvAjG4wdXcXDzH5g6uLpc3rwMweJx7VTeTyk5lXGBl+dd5XrokxC8j42FmVnaT56F0k6qx/n4PEJ+m/jgz6ZAQZVWiGdPn8g/pKKzsBhqNOlTai/Peyhom5gbTYmcnFzg6XlP5d7EZubelHGG5/3E43y8H8Hmv3vz96q/cOvmdeU+EcCJ75C4JyMGD0h2b+7y3iTh4uSIe56JDzFCQp/KEYbSShGSx5R2xN0kx4hOwAVs4lNYFIo4vH7zRrnv5avX+BAXp1zfse8Qrty4hfGjhst7RJnjVrIobnknBm1Bz14i9v17WGbQKnDyhifWHz6LPwZ3V/Y3EApZWyI65r1yPSIqGm8jImFrzZaajyXSgTW15FafdGaiCexTiJQgCwsLlYVpQvSpSjhXlaMJXTsdn9JzcvcyOLrWkukNCfMExMV9SHGcdcFi2L9hGnzuX8SzEF+c2b8KbtWawdy6EEbOOoIhk3YoF3MrW/QcuQwulRJzrikl13LuMv//6OH4UWq2b1kP94pVlBNRhYe9k+OcJ1ezdj3ZN0AMMSpaEvbt2o6KlePbCuvWb4yd2zfhodd9mYe+ctlCVKpSQw5jKUaz+evPuXj00BO7d2zF6RNH0Lxlu2w+a+3gVt5dPtUXT+6FrZs3yPz+hHsTFhaW6r35rHY9nDl1XHZAjoyMxJ5dO1CpSlW5r0BBWyxZOBcPH3jBx/shDu7fg9p16svf55bW1li6eL7ct3PHNpw6cQwtWrXN5rPOudzdyiI8MhL7jsQP8blu679yBCCRPiTSfMTcAMnV/6wGjp05B28/f9kqINKAqleqEP95ri5YtnYjDp84g22792Plhi2oXa2Ksp/An6vWYey3Q5DHJI88VsxLQBmrXKYkwqKisfO/EYRW7juJGmUdZcrWu4hIlYArgRh+dOzyLfixe2vY5beUFf7I/6538+ru2HH6Ci7e90bwi1f43WO3bBEQHZOJNE1P8QnDPBQoUADt27eXsw9//vnnKZqOP9ZeI2fkBq1ivXCsdKNcMemYOJct5z8t6MtO968fw9Ylo2BkbAI9PX30++lv2BaNH+VkXJ+yMvdfDB2a3NkDa3D2wCo50ZWYrKxJp+9SfWo2+4fG6PfT2hw16ViXWvq4+yhxuLqc4tKFs5g7czKMTYzlvZg8fZ4ynadjqwaYvWA5HBydUhzn8fcK7Pxns+wvULhIUUyZuVBWKsWvpn+2bsDBfbvw9u1ruLq5Y/j3P8E6vw2ePQ3FwrnT4XX/DmwL2eHLXgNU5iHQFLfSheHpnfMGFLh44Rxmz5gq742+nj6mzJiN4v/dm3YtG2PuwmXK0YGSWvf3Svy7favy3kybNU95b0TrwJHDB2BkZIT2HbugXYcv5DHi3syfMxOe9+/Ke/NVr374rE49aJKLoz2eeCa2ZGja2YtXMGn2QpgYG0NPXw/zp4yXowLVb9cNK+ZOl8OAJrd83SZs/ncPjI2NYF+4MBZO+w0mJsZ4+vwF5i5dhdv3PfH+/Xs0qF1L9h8Q+375fTbOXLys8jkVy5XF/KkTkFPYuVRCxOmtyIlO3LiPsX9tQR5jI9kquXx0fzgWsUWlAb9i0/ihcC6e2ME+YbQgjyPnVLaJ0YP2zRglX4tgYM2B0wh9+QZlihXGpH4dc3SKkGndzsiJrjaMH7lME6ocP4vc6JOCgbt372L//v04cOAA7ty5g6ZNm+KLL75As2bNPulJf24JBnITbQkGhHevnyHY7y6Kla4AU7PE0R9yq5waDAivXr6Qw086u7jC3CLjTtwJAvz98OLFc7iVqyArl9oqpwYDwquXL+W9KeNSFhYfcW/8/f3w8vlzuJXX3nuT04IB4cWr13jg7QPXMk6wtEg/ZSuBn38gnr18iYpurik6IWurnBwMCM/fvMP9x8FymFErM90aaprBQEoMBtIgUgOOHTsGDw8P7N27F2+TdHLKLAYDOY82BQO6JicHA7ouJwcDuiwnBgOkHcGALmMwoDvBwP/1aOHq1auyhWDfvn2yM/Dw4cPVVzIiIiIioiREah3lgGCgZ8+eOHz4MGxtbWW/gb/++gvlypVTc9GIiIiIiCjHBQOurq4YN24cypQpo/4SERERERGlQp+TjuWMYIBzAxARERER6eg8A2IUoVevXqm/NERERERElLODgUGDBuHKlSvqLw0RERERUTodiDW15FafFAyMGDEC8+bNkxOcEBERERGRDvUZsLGxkWlCVapUka0E+fLlU+7r1auXOstHRERERCTp6X/Sc2xSdzCwZs0aOdOwWLZs2aLcLqbrZjBARERERJSLg4Hjx4+rvyRERERERJSt2NZCRERERKSjPqllQF9fX6YEpebDhw//b5mIiIiIiCinBgO+vr7K1xEREXKY0VmzZuGXX35RZ9mIiIiIiJRy8xCfWhUMlChRQmW9bNmyaN68Odq0aYOuXbuqq2xERERERJTTgoHU5M2bF0+ePFHXxxERERERqdA3YMtAjggGGjZsqNJnIC4uDvfu3UPTpk3VWTYiIiIiIsppwUCfPn1U1kVgYG9vL4MEIiIiIiLKxcFAnTp1MH78ePz999+4ffu2DA7EKELLli1D7dq11V9KIiIiItJ57ECcQ+YZ6N27N/LlywcDAwOMHDlSdhxu164dhgwZov4SEhERERFpkTt37qBatWqwtrbG6NGjoVAo0n2/2D948GDkz58fVlZW8kF7ZGRkzg0Grl+/jgkTJshC3rhxA5MmTcLQoUPx6NEj9ZeQiIiIiEhLREdHywflVapUkcPvi361a9asSfeYdevWwcvLS9axT58+jbt372LatGk5N02oZMmS2Lx5M2JiYmTUIyYhO3HiRIohR4mIiIiIdMn+/fvx5s0bzJkzB6ampvj999/lQ/O+ffumecylS5fwxRdfKOvS7du3lwFBjg0G5s+fj549e8oTXL9+PU6dOoX+/fvDw8ND/SUkIiIiIsoBT/yjo6NVtpmYmMglqZs3b6JmzZqyniy4u7vL1oH0uLm5ydaBTp06ISoqCps2bZKp+Dk2TahJkyYICQmBt7c3atWqJVsHnj59io4dO6q/hEREREREsgOxvsaWadOmwdLSUmVJLZXn7du3cHBwSCyznp7sZ/vq1as0z2vAgAEICwuDnZ2dzMARx4s+ujk2GEhtwjFzc3N1fBQRERERUY4zduxYmf6TdBHbkjM0NEzRWpAnTx5ERESkm3UjOg4/fvwY/v7+eP/+vex4rFUzEBMRERER5dahRU1SSQlKjRgRSIwmlNS7d+9gbGyc5jEi1V4MyFO8eHG5Lloc6tevj9mzZ0MrWgaIiIiIiAgyff78+fPKdV9fX9nXQAQJaYmLi5Mp9wmePHki5/DKDmwZICIiIiKtoA2TjtWrV0/2G1i9erUcQUiMJiT624p+A69fv5ap9eJ1UnXr1sX06dPldjFa54wZM9C2bdtsKS+DASIiIiIiNRF9BlasWIHu3bvLvP+EIfgFMQmZmEugYsWKKsdMmTJFBhA//vijTClq1qyZ7EeQHRgMEBERERGpkXiqL0bdvHr1qhxm1MbGRm5PayZi0Xl47dq10AQGA0RERESkFbQhTSiBGCa0VatWyOnYgZiIiIiISEcxGCAiIiIi0lEMBoiIiIiIdBSDASIiIiIiHcUOxERERESkFfT0+Rxb3XhFiYiIiIh0FFsGiIiIiEgr6Btoz9Ci2oItA0REREREOorBABERERGRjmKaEBERERFpBW2agVhbsGWAiIiIiEhHMRggIiIiItJRDAaIiIiIiHQU+wwQERERkVbgpGPqxytKRERERKSj9BQKhULThSAiIiIiyohvv7Ya+9kOq3YhN8oRaUJbzsdpugiUTJda+thr5KzpYlAqWsV68TuTg783Tzyva7oYlIydSyX4ej/SdDEoFQ6OpTF10wdNF4NS8Us3A+REHFpU/ZgmRERERESko3JEywARERERUUbYMqB+bBkgIiIiItJRDAaIiIiIiHQU04SIiIiISCtwngH14xUlIiIiItJRDAaIiIiIiHQUgwEiIiIiIh3FPgNEREREpBU4tKj6sWWAiIiIiEhHMRggIiIiItJRTBMiIiIiIq3AoUXVj1eUiIiIiEhHsWWAiIiIiLSDHjsQqxtbBoiIiIiIdBSDASIiIiIiHcVggIiIiIhIRzEYICIiIiLSUexATERERERagTMQqx9bBoiIiIiIdBRbBoiIiIhIK3DSMfXjFSUiIiIi0lEMBoiIiIiIdBTThIiIiIhIK7ADsfqxZYCIiIiISEexZYCIiIiItAI7EKsfrygRERERkY5iMEBEREREpKMYDBARERER6SgGA0REREREOoodiImIiIhIK3Bo0RzSMvDixQv88ssv+PDhA3x9fdG+fXu0bt0a9+/fV38JiYiIiIgo57QM9OjRA0ZGRtDT08OIESNgY2Mjt/fv3x/nzp1TdxmJiIiIiNgykFOCgTNnzuDevXt4//69fB0aGornz5/DyclJ/SUkIiIiIqKcEwzY2tri4sWLiI6ORrly5WBsbIzbt2+jUKFC6i8hERERERHlnGBg6tSp+Oqrr2Sq0KZNm3Dp0iV06NABc+bMUX8JiYiIiIgEzkCcM4KB7t27o02bNjA0NESePHnw6tUrXL9+Hc7OzuovIRERERER5ayhRc3MzJSvra2t5UJERERERNrjk9pavL295YhCogOxaBGoUKGC7DsgOhMTEREREVEuDgZ69+6NfPnywcDAACNHjpQpQ23btsXQoUPVX0IiIiIiIso5aUKiNWDz5s2IjIzEjRs3cPToUTm86Pz589VfQiIiIiIiMc+AHucZyBHBQMmSJWUwEBMTg2rVqkFfXx/Hjx9HiRIl1F5AIiIiIiLKQcGAaAHo2bMnTE1NsX79epw6dUrOPuzh4aH+EhIRERERyRmIObRojggGmjRpgpCQEOW6SBd6+vQpzM3N1Vm2XOH1i2CEv32JQsXKwNDQWNPF0TlGNtaoc34bLnzeC5GPgzRdHMoEfme0U2DIE0RERsKxRAkYGPCPNRFRrh9aNKm8efPK/z979gwFCxZEbhMa+AA7Vv6CF6H+qFLvCzTrOipTOWv7N07HjbM7kTefJWKiI9H3x9UoWKSUynsiw99iwc+t8M2vm2BdsGgWnoVuBgLVdi6FqYO9pouic/id0TyfxwGYvmAJgkJC0frzhhjUp0eG9+DE2Qv4c/V6vH//AUP6fYUm9WrL7dHRMZg6bzEuX78FIyNDNG9UD4N695AponFxcfht1nzcuf8AhoYGMDExxoKpE2BtZZlNZ6p9/Pz8MHvuPISEBKNZs2YY0K9fhvfm9JkzWL58Bd5/+ICvB/RHwwYNUuzfvWcPZk6frtwWHR2NWbNn49rVa3KS0CZNGqN/v37yvlHmFbQEWlfXh7U5cMNbgWM3FZk6bkBzfRSySryvN7zjsPeyIsN9lD49ffYZULdP+o3g6emJVq1awcnJCaVKlZKLg4MD7O1zX6XrfWwM1s8bgiIl3DD4t614FvwI18/syPA43/uX4HXjBL6fdRjfzTiA0uVq4/Te5Sned3DzLIS9eZ5FpddtlT3mIHjTHk0XQ+fwO6N5MbGxGDtlJpwdS+Gv2VPhFxCE/UdPZhg8TJmzCL26dMSs38Zi1Yat8A8Mlvs27tgtK/prF8/GjPE/4eS5S9h/LP7zDh4/hZevX2PLioXY9NcCFMhvjX/3H8qW89TWezNh4iQ4lS6NBfPnw9/fH4cPH8kweJg5c5ac8HPq5MlYt94DAYGByv1Xrl7FH7PnQJGsLrlt+3YYGhjir2VLMWnSRJw5ew6Hj6T/s0iVaOTqUlcfT14psOpQHApa6sHdIePKqKEBYG0GzN3xAX9sj18OXlNkuI9Ia4KBPn36wNXVFQ0aNECVKlWwePFiORPx9CRPJHKLB7dOIToyDM27j0F+2+Jo8sX3uHpqe4bHGRgZoV3fSciTN35ytsIlyiIi7LXKe/y8LsPzxnGYmlllWfl12a1B4+C3aJ2mi6Fz+J3RvItXbyA8IgJD+/dE0cJ2+LpnN+w7cjzdY/YePoZK5d3QumkjOJYsjo6tmuLQidNyn+fDR2havy4K2uRHWSdHVKlQHkEhT+Q+0QLw7dd95Yz04omzY8kSePMuLFvOUxtduXwF4eHh+ObrAShSuDD69u6NA4fSD54OHDwE9wruaNG8GRwcSqJt69Y4evSY3BccHIw/lyxFm9atUxzn5fUAjRo1RIECBeBcpgwqVayI4ODEFF/KmGNhwMQIOHxdgddhwPFbcahYKuNgwM4aePoaiIgGomPjl/cfMt5HpDXBwO3btzF69GgMHDgQAQEBaNGiBVasWIE1a9Ygt3kS4AV7R3cYm8SnQtkVc8azYO8MjyteuhIcXKrL1+HvXuHaqX9QtkoTlaenO9f8hlY9foaxiWkWnoHuivRLfHJG2YffGc3z9nsMV2cn5DExkeuicu8XkP734ZHvY1Ryd1OuuziVhpe3j3xdsrg99hw+hndh4fD1D8CFK9dRtYK73FezSiU4lSopX4eEPpWpRnVrVM3Cs9NuPr6+KOviIh+gCaJVXbQOpH+MDyq6V1Cul3Eug0ePHsnXoq/egnlzUbx4sRTHlShRHAcOHkRYWBj8Hj/GpcuXUblSRbWfU24mUnmCXiRW1kUlvoBFxscVya8HC1Pgu/b6+KGjPppX0ZOtDBnto0wQaW6aWnKpT+oz4OzsLCv/o0aNkrMRv3jxAra2tvD19U33OJG/KJakTOQfKyNomsf8YfDzvJRqr/XyNVomruvpyW2R4W9kXnNGrpzYgn0bpqFEmaqoUq+TcvvJPctQwK6k/OxDW2ar8UyIsge/MzlXeEQkCtvaqtwD8dT+XVgYzM3iW16SE51/CxdKPCafaV68ePlKvu7RqR16DRuF1j36y/UOLZuicpLAQVjpsQUb/tmJlo0bypYDSl1ERAQKFSqkcm8MxL159y7NQTjkMXaJx4iR/F68fClfpzdwR5fOnfHNoMH4oktXuS5aDypUSAwqKNEXdfRRIvGfv1KcArjnr0ixLY8REBWb9ufZWAABzxQ4dUch39uulj6qOwPn7yvS3UekNcHAwoUL0blzZwwYMAD9+vWTwYH4hSZmIU7PtGnTMHHiRJVtEyZMgGuz8dC0dn1+Q2yMaqAinD+8NkXHLkMjE8TGRGWqYlOxdnuYWxXErrWTcOGIB2o26YGnwd64fHwzhkz8R63nQJSd+J3JucRoPqKjb1LGRkaIio6BeeqxgKyQGhsapnh/QkW/fFlnfD+wn2wdmDJ3EbbvOYBOrZsr39+9Y1sUty+CectWo2bVSqhdvUpWnV4uuDeqD8CMjI3lg7K0KvYG+gYqxxj/9/6MrF23Hm5urhg2ZIgMBGfO+gM7d+1Cuwz+Vuui/ZfjkOSfv1K1MnpAsjr6hzhAfL3SCwb2X1E96MxdhfwsUeFPbx+RJnxSm0ft2rURFBQkRw6aMWMGtm/fjtWrV2eYJjR27Fi8efNGZRHbcgIzywJyZJLki9guhjlMKiYyHAYGmWvNMDQyhnPFhmjcYbjMm1YoFNi1ZgKadBwBC+tUHkMQaQl+Z3IuCzMzvH77VmVbZFQUjFKr7fzH3Fz1mIjIxPcfPnkWvbt2kv0DRIVfdDJO3gfBNG8efF6/jgwQ9h05ofZzyi1EhV/87UtKDM9tmCxASO+YyIgI2UcjI8dPHEeP7l/CysoKxezt8WW3rjiYQf8EXRUeDbwJT7mERwKm8RldSsaG8QHBR31+lALmeT9+H2mvO3fuyIl5ra2tZWq9+FuWGWKEts8++wyzZ2dfC/gnJ0DJpv//nv7Vr18frVu3zvCXk0gJsrCwUFni04RyLnuH8gjwvqlcf/UsEO/fxyCvWfpPOM8fWoub5xNHshEVIdFM/+ZFMB4/uIqDm//A1MHV5fLmZQgWj2un8n4ibcXvjOa5ODninudD5brI5Rej2KSVIiSPKe2Iu0mOeejjhwI21vK1QhGH10kqoy9fvcaHuPja0NK/PXDt1l3lPhFAGHDovzSVcSqD+56eyvUnT54gNoN7U6aME+7fTzzG29sHBWxsMvxZcXEKvH7zWuW+xX1sLVbHBb9UoKhN4r9ny3zxIwxFxjeapal3E32YJ+naZF9ATwYXGe2j3CE6Ohpt2rSRg+xcuXIF9+7dy3S/2qVLl8rgf8SIEcguubc3hJqUcK4qR0a5djo+PeHk7mVwdK0FfX0D5ZjncXEphwGwLlgM+zdMg8/9i3gW4osz+1fBrVozmFsXwshZRzBk0g7lYm5li54jl8GlUsNsPz8ideN3RvPc3coiPDJS+YR+3dZ/ZR6/SFERaT4fUqkQ1v+sBo6dOQdvP3/ZKiDSgKpXis8vd3d1wbK1G3H4xBls270fKzdsQe1q8WlAtgVsMHvJcng+9MYDH1/sOngEDWrXzOYz1h7ly5eTfQAOHTos1zdt3iJH+TEwMJAdfT98SPndqFO7Nk6eOgVfXz/ZiiBSfapUqZzhzyrn5oZVq9fg2PHj+HfnLqxdtw41a9bIkvPKrfyfxY8mlDCcaG1XPfiFigA5fr/Yl9oUEc/fKNCyqj6K5AfKl9RDDWc9XH2kyHAfZW6eAU0tmbV//35ZoZ8zZw4cHR3x+++/Y+XKlRkeJ0YH+/nnn2U6fvJ0whw/6VhuZmBgiHb9JmPrklFyfHM9PX30++lv5f7fh9aQecxiGMSkRCWlbquvsW3ZaHz48F5OvFSnRX/5pDP5REmikmRhbQeTPPmy7bx0yV4jZ00XQafwO6N5hgYG+HHoN5g0eyGWrvGQf8TmT4nvmyU6Aa+YO105AlCC0g4l0Kl1Cwz84WcYGxvBvnBhtG/RVO4bOXgA5i5dhfnLV+P9+/doULuWTBUSOrRshtBnL/DjxOmyn0LX9q3RqO5nGjhr7SAq/d99OwLTZ8zEilWrZAv7zBnxw3KLjr6LFy6QlYekxFw+7du1xYhvv5X9C4oWKYLWrVpl+LOGDx+GRYsXy6FHxX2rV7cuunfrlmXnlhuJSv/eS3Fo/5k+GlfUk+vrjyUG06M6GWDFgQ8IVR0FGUduKNCmhh6+aqQvU5CO3lTgtp8iw32Us0WnMRBO8iyXmzdvombNmrKzv+Du7i5bBzLy3XffoUSJEnKkznPnzsl0oeygp8hkEpP4g5zeDIniY8T+1J5qZGTL+ZzfbPnu9TME+91FsdIVYGoW33Sem3Wppc9KdA7VKtaL35kc/L154nkdOcWLV6/xwNsHrmWcYGmR9qgzSfn5B+LZy5eo6OaaohOytrJzqQRf7/ihOHOKly9f4uGjR3KYUZEymxmP/f3x4vkL2bqQnU8Ns5KDY2lM3ZTzB9nPlwcobA05zGhGKUK5xS/d4ltzc5pXUwdr7GfPjy2U6kA4v/32m8q2H374AVFRUXIergSin+2DBw9kH4LUnD9/Xlb+W7ZsKdOLNm7cKGcoX7RoEbJapn/TZzRsaG4nRjdxrqg6/TsRpY3fGc2zsbZCraoZp5MkJeYUEAtlrfz586NG9fh5NTKrRPHicqHsFx4FPOJ8bTpv7NixGDlypMq21Pq+ij60ybeLuUVEimBawcDy5ctRo0YN7NmzRz5c//rrr2UrwfDhw+WonTkiGBAFIiIiIiLSGA0OUGCSSkpQWsG+GE0oKTGXiBgWOC2BgYGyVSAhC6dYsWKyNUHM55XVwQA7EBMRERERqYkYUlSk/STNrhF9DUSQkBZ7e3s5QEACMaCASCcsWlS1z1yODQaePn2qjo8hIiIiItJq9erVw9u3b+UcXIIYTahJkyZyAIHXr1+n2r+2e/fuMlXo6NGjePz4MYYMGQIXFxfZ+ThHBgOiR3TlypWxdetWud64cWO4ubnJjhFERERERLrK0NAQK1aswLBhw1CgQAHs3LlTTtIriD4Dt2/fTnHM559/Lt8zePBgGQQ8fPgQ27ZtS3fwHrWV91MOGjhwoJxorGnT+GHnLly4gMmTJ2PQoEE4duyYustIRERERKQ12rZtK/P9r169KocZtflvosD0BvHs37+/XLLbJwUDN27cwJYtW2BpGT+jaL58+WRvZ1dXV3WXj4iIiIhI0tPXnu6udnZ2aJWJOUE07ZOuaPny5VNMq7x+/XqZKkRERERERNrhk1oGxCQKLVq0wLp16+Dg4CB7Sb969QoHDhxQfwmJiIiIiCjnBAOVKlWSnYVFh4gnT57gq6++ks0gmZ1FkYiIiIjoY+lpcJ6B3OqTgoHQ0FDZWVjMkiaGRxLTordv3x4LFy6Era2t+ktJREREREQ5o89A3759ZW/oy5cvy8Dg3LlziIqKktuJiIiIiLKEnr7mllzqk1oGROX/5s2bKFGihFwX0yXPnz8f5cqVU3f5iIiIiIgoi3xSmNOgQQM5tGhSmzZtUs47QEREREREubRlICQkBGPGjMGiRYtgb28Pf39/BAcHy0kVGjVqJN/DyceIiIiISJ3YgTiHBANDhgxRf0mIiIiIiCjnBwO9e/dWf0mIiIiIiChb5d6u0UREREREpP6WASIiIiKibKfP59jqxitKRERERKSjGAwQEREREekopgkRERERkVbQ0+PQourGlgEiIiIiIh3FlgEiIiIi0g7sQKx2vKJERERERDqKwQARERERkY5iMEBEREREpKMYDBARERER6Sh2ICYiIiIiraCnz6FF1Y0tA0REREREOootA0RERESkHfT4HFvdeEWJiIiIiHQUgwEiIiIiIh3FNCEiIiIi0g7sQKx2bBkgIiIiItJRbBkgIiIiIq2gxw7EascrSkRERESkoxgMEBERERHpKAYDREREREQ6isEAEREREZGOYgdiIiIiItIOHFpU7dgyQERERESko9gyQERERERaQU+fz7HVjVeUiIiIiEhHMRggIiIiItJRTBMiIiIiIu2gxw7E6qanUCgUav9UIiIiIiI1i1g1QWM/27TfRORGOaJl4O6jEE0XgZJxK10YW87HaboYlIoutfSx18hZ08WgVLSK9cKRW9GaLgYl08TdBGfvhWm6GJSK2q5meHrviqaLQamwda2q6SJQNmGfASIiIiIiHcVggIiIiIhIR+WINCEiIiIiogxxngG14xUlIiIiItJRbBkgIiIiIu3AoUXVji0DREREREQ6ii0DRERERKQV9NhnQO14RYmIiIiIdBSDASIiIiIiHcU0ISIiIiLSDnp8jq1uvKJERERERDqKwQARERERkY5iMEBEREREpKMYDBARERER6Sh2ICYiIiIi7aDPGYjVjS0DREREREQ6ii0DRERERKQV9Di0qNrxihIRERER6Si2DBARERGRdmCfAbVjywARERERkY5iMEBEREREpKMYDBARERER6SgGA0REREREOoodiImIiIhIO3BoUbXjFSUiIiIi0lEMBoiIiIiIdBTThIiIiIhIO+hxngF1Y8sAEREREZEa3blzB9WqVYO1tTVGjx4NhUKR6WNfv36NwoULw8/PD9mBwQARERERaQd9fc0tmRQdHY02bdqgSpUquHLlCu7du4c1a9Zk9nAZPDx58gTZhcEAEREREZGa7N+/H2/evMGcOXPg6OiI33//HStXrszUsadOncKuXbtgY2OD7MJggIiIiIi0Z2hRDS3R0dF4+/atyiK2JXfz5k3UrFkTpqamct3d3V22DmREfNbAgQOxYMECmJmZIbswGCAiIiIiysC0adNgaWmpsohtyYkgwcHBQbmup6cHAwMDvHr1Kt3PFy0IZcqUQdeuXZGdOJoQEREREVEGxo4di5EjR6psMzExSfE+Q0PDFNvz5MmDiIgI2aE4Nffv38fSpUtx/fp1ZDcGA0REREREGRAV/NQq/8nlz59fjiaU1Lt372BsbJzq+8VIQ9988w2mTJmCIkWKILsxTYiIiIiISE3EkKLnz59Xrvv6+sr+ACJISI2/vz/OnDkjRxGysrKSi9gm+hps2LABWY0tA0RERESkHfRz/qRj9erVk/0GVq9ejb59+8q+AE2aNJH9BsQcAubm5vJ1gqJFi8qAIak6depg06ZNqFixYpaXl8EAEREREZGaiD4DK1asQPfu3eXTfn19fZw4cULuE30GRL+ApJV88f6SJUum+Ax7e/tsGVWIwQARERERkRq1bdsW3t7euHr1qhxmNGHegMzORJxdsw8LDAaIiIiISDuIMf+1hJ2dHVq1aoWcTnuuKBERERERqRVbBoiIiIhIO+jl/A7E2uaTWgZiY2Nlz+gaNWrIHtB3795F9erVZW4UERERERHl4mBgyJAh2LJlC/r16ycnUTA1NcVnn32GgQMHqr+ERERERESUc4KBbdu2Yfv27bLyL8ZJFcuYMWNw8eJF9ZeQKIu8fhGMIN87eP8+RtNF0UlGNtZo+OAo8pYoqumikBq8fBaCx9538T42VtNFISKirO4zUKxYMZw6dQqOjo5yXU9PT6YKOTg4ILd47OeDRfNm4ElwEJo0a4Ve/QbJ88zIuTMnsGbFEnz48B59+g9B3QaNVfZHR0XhuyF90av/INSqXV85zFTPrq0RER6ufF/3nv3QuVuvLDiz3CU08AF2rPwFL0L9UaXeF2jWdVSm7tP+jdNx4+xO5M1niZjoSPT9cTUKFiml8p7I8LdY8HMrfPPrJlgXZIVV3YFAtZ1LYepgr+mi6Jxg/4dY9+d4PHvij88adUSHniMz9Z3Zu3UJTuz1kN8Xt0p10Wv4VOTJm0/u275mFi6e2o18ZpaIjorEiAnLYVc09/w9yC6Bjx9h1aKJeBoSgHpN2qNz728zdW+ER543sXLhRExb/I9y28oFE3D2+J4U7525bDcK2BZRa9lzC5/HAZi26C8EhjxB6yYNMaR39wzvwfFzF7F4jQfev/+AYX17oEndz+T2iMhILFi1HuevXIexkRG6tmuJL1o1k/uG/zoFN+7eT/FZp3d4ZNGZEam5ZWDmzJkYPHgwatWqhYiICIwcORJfffUV/vjjD+QGsbExmDbpZziWLoNZ85chwP8xjh05kKkAYt6sqejcvSfGT56FTR6rEBTor/KezRvWwK5IUWUgIIQEByJfPjOs27xbubTv1C1Lzi03eR8bg/XzhqBICTcM/m0rngU/wvUzOzI8zvf+JXjdOIHvZx3GdzMOoHS52ji9d3mK9x3cPAthb55nUel1W2WPOQjelLKSQln/u23p9OEoXsoVY6ZvwpNAH1w4sTPD4y6d3osrp/dh6C9L8MucHXgS5INDO1bKfQ/uXsada6cwcdE+TFiwG2Ur1FLuo4+7Nwt+/x4lS5XF+FnrEBzogzPHdmfqWD/v+1g0fZT8nZjUVwN/wqL1J5TLd78uQKHCxZHfplAWnYV2i4mNxU+/z4ZzqZJYMWsK/AKDsO/YqQyDh8lz/0Tvzh0we8IYrNy4Df5BwXLfH0tXI/hJKJbOmIifRwzE6k3/YM+R+ImnZvwyCvvW/6VcRg3qh8rlXLPlPLWevr7mllzqk86sefPmsiWgTZs26N+/PypVqoRz586hadOmyA2uXbkon9L3HTAUdoWLokfvATh6aG+Gxx05tBfl3Cvi82atUaJkKbRo3QEnjx1S7vf1eYT9e/7FgEEjVI57+MATzi5uyGdmrlyMjIyz5Nxykwe3TiE6MgzNu49BftviaPLF97h6anuGxxkYGaFd30nIkzd+Vr/CJcoiIuy1ynv8vC7D88ZxmJpZZVn5ddmtQePgt2idpouhc+5dP4PIiDB06j0KBe2Koe2XI3DuaMYB9OvnT9Bz6BSUdCoP28LFUfmzZgjw85T7DA2N8eXACchrGv99sndwQXiy7xNl7Pa1s/LedO33PWwLF0PHHsNw+si/GR4nWmIWzxiFRi27pNhnYpIXpvnMlcvh3R5o1+0b6BsYZNFZaLcL124iLCICw/p9haKFC2Fgjy7Y+1/lPS2icl+pvCvafN4QjiWKo2OLpjh44owMLI6fu4ChfXqgsG1BVCrnilZN6uPMpavyONO8eWCeL59c8uXNi627D2BAj87ZdKZEakgTWrx4sWwJ+Pnnn1PsCwsLy5apk7OSn683yri4wiRPHrle0sERgf6PMzzusY83KlWtoVx3KlMWWzb+rUwFWrpwNpzLusHr/l3ExMTAoVRpue/RA08ZEHzVpRUMDY3QtHlrdO/ZP9PNw7rqSYAX7B3dYWySV67bFXPGs+CMR7QqXrqS8nX4u1e4duof1Pz8K+U28XRt55rf0KrHzzi0ZXYWlV63RfoFaroIOinwsRdKlkn8zhQtUQZPAjP+zjTt0F9l/WnwY9jaFZevSzlXUG4Pe/sK54/9iwYtv1R72XO7AL+HKFWmvKzAC8VKOiEk0DfD4wwMDPHztNUIDfHH6SNpt/L4PryLZ0+DUb1OfJoKpeTt9xhuZUojj4mJXHcsWVy2DqTnkd9j1Kic+B0o6+SINVv+QXh4hEwbsi0QP+usoK+vL5fkTp6/DJv81ijvUkat55NrsW6UM4KBRYsWoUqVKnj8+DFcXV1Rvnx5uV2kDq1duxYzZszAsGHDUhwXHR0tl6RM/vvSacL0yb/gzu0bKbbr6xugTr1GynVRKRdf4LB372Bmbp7m54mUqUKFCivX85qa4uXLF/L12VPH8PDBfdRt0ATBQQHwWLsCbdp3lulAYr1qjVpo3fYLPAkJwpyZk1C8hAPq1Fftb6CrPOYPg5/npRTb9fT1Ub5Gy8R1PT25LTL8jewLkJErJ7Zg34ZpKFGmKqrU66TcfnLPMhSwKyk/m8EAaaNlM7/Fw7tXUmwXv8eq1G6e7HebASLC3sLUzCJTnx0a7Iebl45izMzNKtvPHtmGratnonTZyvisUQc1nEXutHDaSHjeiX86nJS4D9XrNE3x+yw87C3ypXNvDI2MYG1jK4OB9BzZtxkNm32RamWU4oVHRMqn+Mn/9r8LC4e5Wb40jylia6tcz2eaF89fvYalhbkMBERLgGg1iIyKwolzl9C1bYsUn7F930F0bp1yO1GODgZatGiBZs2awc3NDQ8fPkSfPn0wa9YsGQjs3btXjjKUWjAwbdo0TJw4UWXbhAkT0PkrzQxJOmjYD4iJUQ1OhD07t6d4Km9kbIzo6Kh0gwExqpL4xZzA2NgYMdFR8vWhA3vwWZ0G+H70r3K9SvVamDB2JJq1aItxk2YqjylkVxit2nTCubMnGQz8p12f3xCbyn06f3htivtkaGSC2JioTAUDFWu3h7lVQexaOwkXjnigZpMeeBrsjcvHN2PIxMROeETapvs34+X3ILnj+zygh2TfGfl7KjJTwUBcXBzW/zkBnzXuiCLF4ls2E1Sv3xYW1gWxafkUnNi/EQ1adFfDmeQ+vQb/kurvs8N7Nqa4N0ZGJvJvSHrBQGaEvXuDG5dO4Mv+o/6vz8ntxN/wJH/CJRMjI0RFR6cZDMQfk1iVEh2FxUNPEUT8NPRrTJr7J05fvIIHPn4ydahp/doqx/v6ByIwJBR1qlfJmpMiyqpgQFT6z5w5I1sEXr58iSJFiuDbb7+Vk5G5u7vDz88v1ePGjh0rOxsnbxl4FPASmmBlnT/N7QGPVZtnIyMjVCr6qRGBwts3ibmykRERMu1HePH8GRo2SXwiV8qxDN6/j8Xz509RrHhJlc+xtLLCyxfsuJrAzLJAmtufBj5U2RYTGQ4Dg/TvUwJDI2M4V2yIxu9e4fzh9ajR+EvsWjMBTTqOgIV14pMeIm1jYWWTxvYCCPZ/pLItOjICBv/9nsrI/u3LEBH2Ro5AlJzo51S+Sn2ZKnRi3wYGA2mwTOPeiO1B/qopW1Hi91km7016rl04Bqeylf7voCK3szAzg49/gMq2iMgoGBqmXVWyMMuH12/fJb4/KlL5/moVy2Pb8vkICH6CUZNmoFu7Vshnaqpy/JHT51C/ZjUYGLDFJtP0eK3U7ZOuaOHChWULwKNHj+T/8+bNi7dv38LS0hKRkZFyPTWi4m9hYaGyaDJNKC1OZVzg5XlXuR76JESOnW1mlnargFDaSfU4H59HyG8TX5G1KVBQpRXi2dMn8qm26CwshhpNmj7l5XkPBW052kNG7B3KI8D7pnL91bNAOWdAXrP0WwXOH1qLm+cTR7IRwYN4ivPmRTAeP7iKg5v/wNTB1eXy5mUIFo9rp/J+Im1VwtENvg8SvzPPQwNlHxkxJGhGbl85gWO71+HrUXOUfQ6E43vX4/LpxAEWROWVqSgfz6G0K7y9binXn4UGyQdGZmqowF8+exhVaiamvlLqXEqXwl2vxGA5OPQpYt7HyiAh7WMccccr8aHUA5/HKJjfWrluYmyMgOAQ+fe+S5vEB4IJjp29iHo1q6n1PIg+1if9xt6wYQN2796NChUqyOFExehCYiQhMf/AgAEDULVqVWgz13LuMv//6OH9cn37lvVwr1hFNgcK4WHv8OHDhxTH1axdT/YNEEOMipaEfbu2o2Ll+C953fqNsXP7Jjz0uo/goECsXLYQlarUQP78NrCyssZff87Fo4ee2L1jK06fOILmLdtl81lrnxLOVeVoQtdOx6f0nNy9DI6utWTubcI8AXFxKe+TdcFi2L9hGnzuX8SzEF+c2b8KbtWawdy6EEbOOoIhk3YoF3MrW/QcuQwulRpm+/kRqVtp1yqIigzD+ePxo9Qc3LECzu41lKPLRIjvTCq/28QQpKvmjUGX/j/BysYOUZERMrVIKFDIHtvWzMSDO5cQGuSLIzvXoFKt3DGyXHYq41YZkRHhOH10l1zfu20VXN2rJ7k371K9NxkRaUZed6/BuRzTUDJSwc1F9gHYe/SkXF+3bSequpeTT+3fhYfjw4e4FMc0qFUNx86ch/djf9mKsH3vQVSv5K6SWieGFB3Q/Qtlx+QEwU+eIvTZc5RzccqGs8tFOLRozkgTEulBZ8+eVdnm5eUFa2tr7NmzBw0banfFSYzOMGTEaMydORlrVy2Bnp4+Jk+fp9zfs2sbzF6wHA6Oql9gMTpQq7ZfYPS3A2V/gcJFiqJ5q/Zyn5i47O3bN5g17Te8ffsarm7uGPrtaLlv6HdjsHDudPwyejhsC9lh5JjxcCtfMZvPWjvvU7t+k7F1ySg5J4C4T/1+ih+9Sfh9aA2Z+y+GDk1KVOzrtvoa25aNlpPDicnK6rToL59mJp9cTAQWFtZ2MMmTer4o/X/2Gjlrugg6953pMeg3rJ4/BjvWzZFPK7/7bZVy/+g+dfDTzC0o5uCictyZI9tk5X/tItHnKb7fU/6CRTD5zwMoX7UBmrbvjzULxuLD+/eyP0GTtn2y/dxyw73pM3Qcls35GVv/nid/n/045S/l/mFfNcBvczaguMPHfWceed2S/UFs7TjBX0YMDQwwZugATJyzGEv+3iDvwYIpv8h9Lb/6BqvmTIWTg2pab2mHEviiVXN8PWocjI2NYF/YDh2af67cf+DEaegb6KNFo3opft61O/fgVKqEbD0g0iQ9hRjz8iN5enpi1KhRMgBIeEIuPiY4ODjFaEGZcfdRCHKiVy9fwPvRAzi7uMLcIuNm9AQB/n548eI53MpVgFEG/QxyKrfShbHlfMqnIDnRu9fPEOx3F8VKV4CpWWLzbG7VpZY+K9E5VKtYLxy59fG/A7Pbm1fPEeBzTw4zamae++fSaOJugrP3wqANxL0Rk4g5likPM4vcf29qu5rh6b2UI19p0otXr+Hl7SuHGRWjAmWGb0Agnr94hYpuZVU6FGszW9ecmeURdWCFxn52nuYDkBt90r9YMXpQ3bp1Zd+B169fo1+/frJj8PTp05GbWOe3QdXqtT76ONEhOHmnYMo6YkQg54oNNF0MIq1haV0AllVSPqmknHFvKlStq+li6DQbayt8VjVxPprMcChmLxcibfRJCVC3b9/G6NGj5RCiAQEBcqjRFStWYM2aNeovIRERERER5ZxgwNnZWVb+xTCi3t7eePHiBWxtbeHrm/FsiUREREREn0TML6SpJZf6pGBg4cKFchbihBQhERx89tlnaNu2rfpLSEREREREOafPQO3atREUFCRfz5gxAy1btsS7d+/kEKNERERERFmCk46p3Sd3eRdD0iWoX7++uspDRERERETZhOEVEREREZGOyh2D4RIRERFR7peLO/JqClsGiIiIiIh0FIMBIiIiIiIdxWCAiIiIiEhHMRggIiIiItJR7EBMRERERNpBn8+x1Y1XlIiIiIhIR7FlgIiIiIi0goJDi6odWwaIiIiIiHQUWwaIiIiISDvo8Tm2uvGKEhERERHpKAYDREREREQ6imlCRERERKQdmCakdryiREREREQ6isEAEREREZGOYjBARERERKSjGAwQEREREekodiAmIiIiIq3AGYjVjy0DREREREQ6ii0DRERERKQdOLSo2vGKEhERERHpKLYMEBEREZF2YJ8BtWPLABERERGRjmIwQERERESkoxgMEBERERHpKAYDREREREQ6ih2IiYiIiEg76PM5trrxihIRERER6SgGA0REREREOoppQkRERESkFRScZ0Dt2DJARERERKSj2DJARERERNpBj8+x1Y1XlIiIiIhIR7FlgIiIiIi0goItA2rHK0pEREREpKMYDBARERER6SgGA0REREREOorBABERERGRjtJTKBQKTReCiIiIiCgjYRd3a+xnm9Vog9woR4wm5OkdqOkiUDIujvZ44nld08WgVNi5VMKRW9GaLgaloom7CfYaOWu6GJRMq1gvRG35Q9PFoFTk6TIKr2+c0HQxKBVWFRtougiUTZgmRERERESko3JEywARERERUUY4z4D68YoSEREREekotgwQERERkXbQ09N0CXIdtgwQEREREanRnTt3UK1aNVhbW2P06NHIzOCdEydORP78+WFiYoIOHTrg3bt3yA4MBoiIiIiI1CQ6Ohpt2rRBlSpVcOXKFdy7dw9r1qxJ9xgPDw+5HDhwAHfv3sX9+/cxffp0ZAcGA0REREREarJ//368efMGc+bMgaOjI37//XesXLky3WMCAgLw999/o3r16ihdujS6du2K69ezZ4h39hkgIiIiIsrEE3+xJCVSesSS1M2bN1GzZk2YmprKdXd3d9k6kJ6ffvpJZd3LywtOTk7IDmwZICIiIiLtIIYW1dAybdo0WFpaqixiW3Jv376Fg4NDYpH19GBgYIBXr15l6hQfPHiAHTt24JtvvkF2YMsAEREREVEGxo4di5EjR6psS94qIBgaGqbYnidPHkRERMgOxemJi4tDv379MGDAALi5uSE7MBggIiIiIq2g0ODQoiappASlRowIJEYTSkqMDGRsbJzhsZMnT8bLly8xa9YsZBemCRERERERqYkYUvT8+fPKdV9fX9nXQAQJ6dm9e7fsdLx9+3Zlf4PswGCAiIiIiEhN6tWrJ/sNrF69Wq6L0YSaNGki+w28fv0aHz58SHGMGEq0e/fuWLhwIYoVK4awsDCZVpQdGAwQERERkXbQYAfizBJ9BlasWIFhw4ahQIEC2LlzJ2bMmCH3iT4Dt2/fTnHMX3/9hfDwcPTu3Rvm5uZycXV1RXZgnwEiIiIiIjVq27YtvL29cfXqVTnMqI2Njdye1kzEc+fOlYsmMBggIiIiIq2ggOY6EH8sOzs7tGrVCjkd04SIiIiIiHQUgwEiIiIiIh3FYICIiIiISEcxGCAiIiIi0lHsQExEREREWkHxEUN8UubwihIRERER6Si2DBARERGRdmDLgNrxihIRERER6SgGA0REREREOoppQkRERESkFRR62jMDsbZgywARERERkY5iMEBEREREpKMYDBARERER6SgGA0REREREOoodiImIiIhIK3AGYvXjFSUiIiIi0lFsGSAiIiIi7cChRdWOLQNERERERDqKLQNEREREpBXYZ0D9PumKfvjwAfPnz0ejRo3g5uaGxo0bY8mSJXI7ERERERHl4paBb7/9FgcPHsTQoUNRtGhRBAQE4I8//oCXlxfmzZun/lISEREREVHOCAY2btyIkydPoly5csptTZo0QYMGDRgMEBEREVGWUIAdiHNEmpCDgwOCg4NVtgUFBaFYsWLqKhcREREREeXElgEnJyd07NhRLiIA8PPzw7///ovOnTtj0qRJ8j3jx49Xd1mJiIiIiEjTwUCePHlkxV8QLQTGxsbo0qWLXPf19YUex4AlIiIiIsqdwcDq1avVXxIiIiIiIsr5fQZevnyJX375RQ4lKlKE2rdvj9atW8PT01P9JSQiIiIi+m+eAU0tudUnndmXX36JW7duyXSg4cOHw8rKCgUKFEC/fv3UX8JcJiQ4CD7eDzknQw4XGPIED3x88eFDnKaLQkm8fBaCx9538T42VtNFISIi0t00oTNnzuDevXt4//69fB0aGornz5/LjsW5xWM/XyyYOwshIUH4vFlL9On3Tab6Qpw9cxKrly/F+w8f0G/AINRr0Ehuj4uLw6zpk3H/3l0YGhjAJE8eTJ0xB1ZW1lAoFOjRpR3Cw8OVn9OjZ1906f5Vlp6jNvF5HIDpC5YgKCQUrT9viEF9emR4P06cvYA/V6/H+/cfMKTfV2hSr7bcHh0dg6nzFuPy9VswMjJE80b1MKh3D+jr68v79Nus+bhz/wEMDQ1gYmKMBVMnwNrKMpvONHcI9n+IdX+Ox7Mn/visUUd06DkyU9+fvVuX4MReD8RER8KtUl30Gj4VefLmk/u2r5mFi6d2I5+ZJaKjIjFiwnLYFXXIhrPRPUY21qhzfhsufN4LkY+DNF2cXO1h6EtM2HES/i/eomMVF3zfrHqm+929jYxGhwVbsfabdihqba7cfviODzZduoeV/VpnYclzP2//IExe8jcCQ5+hbaPaGN6jU6buzS0vb3nc1nnxA6okJf7GfDNhFhpWr4webT7PopLncuyXmjNaBmxtbXHx4kVs2bJFzjUgOhDfvn0bhQoVQm4QGxuDKRN/hWNpJ8yevwQB/o9x9PDBTAUQc2ZOk5X43yZPx4b1axAYGCD3HT96CK9fvcSKNRvw12oP5M9vg/17dsl9wUGByJfPDB5bdiqXDl/Ed8gmICY2FmOnzISzYyn8NXsq/AKCsP/oyQyDhylzFqFXl46Y9dtYrNqwFf6B8cPhbtyxW1b01y6ejRnjf8LJc5ew/1j85x08fgovX7/GlhULsemvBSiQ3xr/7j+ULeeZW4jvz9Lpw1G8lCvGTN+EJ4E+uHBiZ4bHXTq9F1dO78PQX5bglzk78CTIB4d2rJT7Hty9jDvXTmHion2YsGA3ylaopdxH6g8Equ1cClMHe00XJdeLef8BI9YfRNkiBbBxcAf4PHuFndcfZPr4uQcv4nlYpMq2sw8D8Os/J8Vg7PR//t35YeZiuJQqgTW//wzfwBDsOXEuw+Pu+zzGj7OXIPb9+1T3/3P4FMIiItG1RfyDQiKtDQamTp2Kr776CoMGDcKYMWNw6dIldOjQAT/++CNyg6uXLyEiPBz9vx6MwoWLoGfv/jhyaH+Gxx0+uA/lK1RE0+atUNKhFFq1bocTRw/LfaIF4OtBw2FoaCifQIv97969lfsePvSCc1lXmJmZKRcjI+MsP09tcfHqDYRHRGBo/54oWtgOX/fshn1Hjqd7zN7Dx1CpvBtaN20Ex5LF0bFVUxw6cVru83z4CE3r10VBm/wo6+SIKhXKIyjkidwnWgC+/bqv8j45liyBN+/CsuU8c4t7188gMiIMnXqPQkG7Ymj75QicO7ojw+NeP3+CnkOnoKRTedgWLo7KnzVDgF98PyRDQ2N8OXAC8pqayXV7BxeEh73O8nPRRZU95iB40x5NF0MnnHkQgLDoWIxqXgvF8ltgeJNq2HHVK1PHXvULwQlPf1iZmii3+b94g+l7zqFbDdcsLLVuOH/jLsIjIvFdr86wtyuIwd3aY9fxs+keExkVjZ9mL0XnZg1T3f/s5Wss2fQvRvXtJh9I0adRQF9jS271SWfWvXt3vHjxQqYGiY7DIj3o+vXrMjjIDfx8feDsUlam8gii4i5aBzLi6+sNd/eKynUnZxd4P4p/ylOlWg2UciwtX4eGPsHZM6dQo1Z82spDL0+5fNm5LXp174T1f6+SqUMUz9vvMVydnZDHJP6Pnqjc+wUEpnvMI9/HqOTuplx3cSoNL28f+bpkcXvsOXwM78LC4esfgAtXrqNqBXe5r2aVSnAqVVK+Dgl9KlON6taomoVnl/sEPvZCyTLuMDbJK9eLliiDJ4HeGR7XtEN/lHKuoFx/GvwYtnbF5Wux3ckt/j6EvX2F88f+RYXqjbPsHHTZrUHj4LdonaaLoRMePHkBd3tb5DWOz9gtY5cfPs9eZ6pFYfLOMxjTqhZMjY2U261M88BjUHuUKmidpeXWBQ/9AuDmVAp5TOIfzDmVsIdfYEi6x4gK/vLJP6KiS/zf+uTm/r0FdgVsEPrilUwlItLqPgOCeHqdwNraWi7a5vdJ43Dn9s0U28UT4Tr1EiN7kSMotoW9ewcz88S8zOQiIyJQyK6wct3UNB9evnyh8h6Pdavxz9ZNaPx5c1SoWFmZJlStRi20adcRISHB+GPGFBQvWRL16rMZURBPZwrb2qa4H+/CwmCe5N9hUhGRkShcKPGYfKZ58eLlK/m6R6d26DVsFFr36C/XO7RsispJAgdhpccWbPhnJ1o2bihbDiilZTO/xcO7V1JsF/emSu3mye6XASLC3sLUzCJTnx0a7Iebl45izMzNKtvPHtmGratnonTZyvisUQc1nAUlF+mXfqBN6iNaBZLm+ovvioGenuwLYJE38Yl/citO3kCJApZoXt4R8w9dUm5P7xj6OOGRUShia5Pi787bsHBYmMX3Y0rOyNAQtvmtERDyNMW+2w+8cfTCVXxWqZzsg7Dqn32oWcEVo/t1z9LzIMqyYCAyMhITJ07Etm3bEBISIvsKiOFFxTbzdCrL0dHRcknK5L+nvZowZPj3iI6JSbF9985/kLx7ipGxMaKjo9INBkSFx9DIKNkxqufb8YtusLcvjmVLFqBq9RqoXuMzTJg8XblfBBOt23bAuTOnGAz8x8BAX3b0TcrYyAhR0TEwTz0WgIG+PowNDVO8P6GiX76sM74f2E+2DkyZuwjb9xxAp9aJFdjuHduiuH0RzFu2GjWrVkLt6lWy6vS0VvdvxiM2JirF9uP7PKCX7BtkaGwsOwVnJhgQHezW/zkBnzXuiCLFVJ+wVa/fFhbWBbFp+RSc2L8RDVrwDylpLwN9PRgZqjbQGxsaIDL2fZoVe5+nr7D18n1sHsJgOKv/7hgnqyIZGxshKiYGFkg9GEjPv0fPwK20A+aMGSYDi/aN6qDdsJ/RpXlDlChip8aSE2VTmtA333yDo0ePYsaMGThx4gTmzJmDCxcuyO3pmTZtGiwtLVUWsU1TrKzzo1AhuxSLaOV4++aNynujIiNUKvqpEYHQ2zevVVoKRO55Unnz5kX9ho1lhf/IoQOpfo6lpTVevnj+f51bbmJhZobXb+P7VySIjIqST2HSYm6uekxEZOL7D588i95dO8n+AaLCLzoZJ++DYJo3Dz6vX0cGCPuOnFD7OeUGFlY2sLEtmmKxsCqAd2/jW2ESREdGwMAw/e9Pgv3blyEi7I0cgSg50ZemfJX6aN11KM4fy7gfAlFOZpnXBK/CVQPqiJhYGBmk/qdZpI9O2nUaw5pUha3Fx1dIKfPE0/9Xb1X7iyX9O/Kxnr58JVsFEkYjKlQgP6wszGQrAZFWBgO7du3Cxo0b0alTJ1SrVk22CqxduxYHDqReuU0wduxYvHnzRmUR23IaJycXeHreU66HPglBbGwszMzSbhUQSpdxhuf9xON8vB/BxqaAfP33qr9w6+Z15T5DQyPZ5ChaDkYMHqDSguDleRcFbXPHyEzq4OLkiHueD5XrIpdfjPSQVoqQPKa0I+4mOeahjx8K2MSnsikUcXidJNh7+eo1PsTFzyew9G8PXLt1V7lP/OIXT+8o80o4usH3QWL63fPQQLyPjZFDgmbk9pUTOLZ7Hb4eNUfZ50A4vnc9Lp/eq1w3+O/7Q6TN3OwL4lZAYkpJ4Ku3sj+ACBJSE/ImDNcfh8pRhOpM/VsuYlvnxdux7+ajbCx57ufqWBJ3HsT3MxOCnz5HrGixSSNFKCMifSg6JnF+lIioKJlyVNDaSi3l1SUKPT2NLbnVJ/01rVq1Ks6dUx1iS4woVLFiYufZ1IiUIAsLC5VFk2lCaXEr7y6f6ic8ud+6eYPM7zcwiO/9HxYWluqkYZ/Vroczp47LDsgilWrPrh2oVCW+02OBgrZYsnAuHj7wkpOOHdy/B7Xr1Jfnb2ltjaWL58t9O3dsw6kTx9CiVdtsPuucy92tLMIjI5VP6Ndt/Vfm8YtmXJHmk9rEYPU/q4FjZ87B289fPs0RaUDVK8V3TnV3dcGytRtx+MQZbNu9Hys3bEHtavFpQLYFbDB7yXJ4PvSWk47tOngEDWrXzOYz1m6lXasgKjIM54//K9cP7lgBZ/ca0P/v+xMR/hZxqXx/xBCkq+aNQZf+P8HKxk62xonUIqFAIXtsWzMTD+5cQmiQL47sXINKtZpm85kRqVeVEoURFh2Df6/FjyC08uQN1HAsKtMcRb+BhIcUCWzN82HfyG7YPKSjcilong+LejZHA5cSGjqL3KliWSfZb2D3fyMIrdmxH9XKu8h78y48IsW9yUjT2tWw89hpXL59HyHPXmDmio0oWcROdkwm0rRMt3clnV1YpMP07dsXS5cuhYODAwICAmRw0KxZM+QGotI/9NsfMHvGVKxZtQz6evqYMmO2cr+YIGzuwmXK0YESOJRyROt2HfHDt0Pk3AuFixRFi1bt5L6Wrdvh2dNQTBz/E4yMjNC+YxfUrR/fSXnEd6Mwf85MjB39LWwL2WHUmF9RrnziqCq6TkzS9uPQbzBp9kIsXeMBPX09zJ8yXu4TnYBXzJ2uHAEoQWmHEujUugUG/vCzzPO0L1wY7VvEVx5HDh6AuUtXYf7y1XLivAa1a8lUIaFDy2YIffYCP06cLvspdG3fGo3qfqaBs9ZeBgaG6DHoN6yePwY71s2RzeLf/bZKuX90nzr4aeYWFHNwUTnuzJFtsvK/dtGvAMQC5C9YBJP/PIDyVRugafv+WLNgLD68fy/7EzRp2yfbz41InQwN9PFbu3oYs/WYfNovvisJE4XV/X2trOy7FLZReX/SDsdym74eClnkg6lJ5tLwKPN/d34e2BPjFqzAQo/tsh7w54T41MUm/b7Huhm/okzJYpn+vBrurhj2ZUfMWLkBT5+/glNJe0wbOTDTE8wRZSU9RSbHsBSdgzNjwoQJH10IT++cOXrFq5cv5dCgZVzKwsIi8zPQ+vv74eXz53ArX0FW/LWRi6M9nngmpjXlBC9evcYDbx+4lnGCpUX6KVsJ/PwD8ezlS1R0c03RCVlb2blUwpFbqh3Tc6I3r54jwOeeHGbUzFw3msKbuJtgr5GzpotBybSK9ULUlj+QUz1/F4F7wc/hXsxWDg+qS/J0GYXXN3Juv6wXr9/gvo8/yjs5wDKtEStyKauKDZATabJuYudSCblRpmtHn1LJ13bW+fOjavWPTxEpXrykXEi9bKytUKtq/HCsmSXmFBALZT9L6wKwrFJP08UgyvEKmJuinnP8nBqUs9hYWaJOZQ4vTblb7nhUSkRERES5niLF4O/0/+JwHEREREREOirTLQOlSpXCrVu35MzDotNwWp1efHwSh+IiIiIiIlIXhR6fY2ssGFi9ejVMTU3l6zVr1qi9IERERERElEODgfr166f6moiIiIiItBPbWoiIiIiIdNQnBQPe3t7o0aOHnLDp+vXrqFChAsqVK4ezZ+Nn6iMiIiIiolw6tGjv3r3h6uoqZ+odOXIk2rRpAzF32ZAhQ3Dz5k31l5KIiIiIdJ6CszbnjGBAtAZs3rwZkZGRuHHjBo4ePYrQ0FDMmzdP/SUkIiIiIqKcEwyULFlSBgMxMTGoVq0a9PX1cfz4cZQoUUL9JSQiIiIiopwTDMyfPx89e/aUQ42uX78ep06dQv/+/eHh4aH+EhIRERERcQbinBMMNGnSBCEhIcp1kS709OlTmJubq7NsRERERESU04KB5PLmzauOjyEiIiIiShNnIFY/XlEiIiIiIh3FYICIiIiISEcxGCAiIiIi0lEMBoiIiIiIdJRaOhATEREREWU1Di2qfmwZICIiIiLSUWwZICIiIiKtwKFF1Y9XlIiIiIhIRzEYICIiIiLSUUwTIiIiIiKtwA7E6seWASIiIiIiHcWWASIiIiLSCuxArH68okREREREOorBABERERGRjmIwQERERESkoxgMEBERERGp0Z07d1CtWjVYW1tj9OjRUCgUGR6zbds2lChRAkWKFMHGjRuRXRgMEBEREZHWDC2qqSWzoqOj0aZNG1SpUgVXrlzBvXv3sGbNmgyDhx49emDcuHE4ePAgxo8fDy8vL2QHBgNERERERGqyf/9+vHnzBnPmzIGjoyN+//13rFy5Mt1jVqxYgYYNG2LAgAEoX748hg0bhnXr1iE7MBggIiIiIq2g0NPT2BIdHY23b9+qLGJbcjdv3kTNmjVhamoq193d3WXrQHrEMY0aNVKuV69eHVevXkV2YDBARERERJSBadOmwdLSUmUR25ITQYKDg4NyXU9PDwYGBnj16lWan538GAsLCwQHByM7cNIxIiIiIqIMjB07FiNHjlTZZmJikuJ9hoaGKbbnyZMHERERskNxapIfk/D+7MBggIiIiIi0gkKR+Y686mZiYpJq5T+5/Pnzyw7BSb179w7GxsbpHvPs2bNMv1+dmCZERERERKQmYkjR8+fPK9d9fX1l3wJR4c/sMdevX0fRokWRHRgMEBERERGpSb169WQfgNWrV8t1MZpQkyZNZL+B169f48OHDymO6dSpEzZt2oTbt28jLCwMCxYsQLNmzZAdGAwQEREREamJyP8XQ4WK4UELFCiAnTt3YsaMGXKf6DMgKvzJVahQAd9++y2qVq0qWwRE4DBkyJDsKW+2/BQiIiIiIh3Rtm1beHt7y+FBxTCjNjY2cnt6MxFPnTpVTjwWFBSE+vXrZ1ufAQYDRERERKQVFFqU1GJnZ4dWrVp91DGurq5yyU7ac0WJiIiIiEit2DJARERERFpBAc0NLZpbsWWAiIiIiEhHsWWAiIiIiLQCWwbUjy0DREREREQ6Sk+R3hhHREREREQ5hJd3gMZ+trNjMeRGOSJN6InndU0XgZKxc6kEX+9Hmi4GpcLBsTTO3gvTdDEoFbVdzRC15Q9NF4OSydNlFPYaOWu6GJSKVrFeeHdpr6aLQakwr/5xQ2JmF6YJqR/ThIiIiIiIdBSDASIiIiIiHcVggIiIiIhIRzEYICIiIiLSUTmiAzERERERUUbYgVj92DJARERERKSj2DJARERERFpBoWDLgLqxZYCIiIiISEexZYCIiIiItAL7DKgfWwaIiIiIiHQUgwEiIiIiIh3FYICIiIiISEcxGCAiIiIi0lHsQExEREREWoEdiNWPLQNERERERDqKwQARERERkY5imhARERERaQWmCakfWwaIiIiIiHQUWwaIiIiISCsoFGwZUDe2DBARERER6Si2DBARERGRVohjnwG1Y8sAEREREZGOYjBARERERKSjGAwQEREREekoBgNERERERDqKHYiJiIiISCtw0jH1Y8sAEREREZGOYjBARERERKSjmCZERERERFqBMxCrH1sGiIiIiIh0FFsGiIiIiEgrsAOx+rFlgIiIiIhIRzEYICIiIiLSUQwGiIiIiIh01Cf3GYiJiUFoaCgUCoXK9uLFi6ujXERERERElBODgcWLF+OHH35AbGysSjCgp6eHDx8+qLN8REREREQShxbNIWlC48ePx+zZsxEVFYW4uDjlwkCAiIiIiCiXtwyYm5ujUaNGMDIyUn+JiIiIiIhSwaFFc0jLwMKFC/HNN9/g7t276i8RERERERHlrJYBBwcH2ScgwfPnz+Hu7g5ra2tYWFgot/v4+Ki/lEREREREpLlgYM2aNer/6UREREREmcQOxBoMBurXr5/mvqdPn8LW1lZdZSIiIiIiopzaZ+DevXuoXLkytm7dKtcbN24MNzc3PHjwQN3lIyIiIiKS4jS45FafFAwMHDhQthQ0bdpUrl+4cAFt2rTBoEGD1F0+IiIiAmBkY42GD44ib4mimi4KEen60KI3btzAli1bYGlpKdfz5cuH4cOHw9XVFdrM53EApi9YgqCQULT+vCEG9emh0mk6NSfOXsCfq9fj/fsPGNLvKzSpV1tuj46OwdR5i3H5+i0YGRmieaN6GNS7B/T19fHhQxzm/bUKR06elXMzNKlfByMH94ehgUE2nan28fPzw+y58xASEoxmzZphQL9+Gd6b02fOYPnyFXj/4QO+HtAfDRs0SLF/9549mDl9unJbdHQ0Zs2ejWtXr8mhc5s0aYz+/frJ+0aZF/j4EVYtmoinIQGo16Q9Ovf+NsP7leCR502sXDgR0xb/o9y2csEEnD2+J8V7Zy7bjQK2RdRa9tzqYehLTNhxEv4v3qJjFRd836x6pu/J28hodFiwFWu/aYei1ubK7Yfv+GDTpXtY2a91FpacEgKBajuXwtTBXtNF0RmPAkIwafkmBIQ+R/sGNTCiW5sMvzN//XMQmw6dQmR0DGpXKIuJA79Evrx5MtxHpEmfVMMpX758ig7F69evl6lC2iomNhZjp8yEs2Mp/DV7KvwCgrD/6MkMg4cpcxahV5eOmPXbWKzasBX+gcFy38Ydu2FoaIC1i2djxvifcPLcJew/Fv95G7bvxCMfPyyZORmLZ0zC2UtXsP/oiWw5T229NxMmToJT6dJYMH8+/P39cfjwkQyDh5kzZ6F79+6YOnky1q33QEBgoHL/latX8cfsOUgygba0bft2GBoY4q9lSzFp0kScOXsOh4+k/7NIVWxsDBb8/j1KliqL8bPWITjQB2eO7c7UsX7e97Fo+ii8j41R2f7VwJ+waP0J5fLdrwtQqHBx5LcplEVnkbvEvP+AEesPomyRAtg4uAN8nr3CzuuZT+uce/AinodFqmw7+zAAv/5zUgz6TdmgssccBG9KGRBT1oiJfY+Rc1bCxcEe6yZ9D5+gUOw+fTndY/afvYoD569hwehvsGX6j/ANfoo1e45luI9IK4OBxYsXY/78+bIloFWrVjIImDdvHpYsWQJtdfHqDYRHRGBo/54oWtgOX/fshn1Hjqd7zN7Dx1CpvBtaN20Ex5LF0bFVUxw6cVru83z4CE3r10VBm/wo6+SIKhXKIyjkidz35u1bjPthOEoWt4dTqZKoUbkiHvr4Zct5aqMrl68gPDwc33w9AEUKF0bf3r1x4NChdI85cPAQ3Cu4o0XzZnBwKIm2rVvj6NH4X7zBwcH4c8lStGmd8mmml9cDNGrUEAUKFIBzmTKoVLEigoNDsuzccqPb184iMiIMXft9D9vCxdCxxzCcPvJvhsdFR0Vi8YxRaNSyS4p9JiZ5YZrPXLkc3u2Bdt2+gT5b0zLlzIMAhEXHYlTzWiiW3wLDm1TDjqtemTr2ql8ITnj6w8rURLnN/8UbTN9zDt1qaHdrsDa5NWgc/Bat03QxdMa5m/cRFhmJkV+2g32hAhjauSV2nryY7jGhL1/jt2+6o5xjCRQrVBBNa1SEl19ghvuItDIYqFSpkuwsPG7cONl34Ndff4WXlxcqVKgAbeXt9xiuzk7IYxL/B09U7v0C0v+iPvJ9jEruia0hLk6l4eUdP8+CqOjvOXwM78LC4esfgAtXrqNqBXe5b9iA3ihil/hEMyAoGPaF7bLozLSfj68vyrq4IE+ePMo5L0TrQPrH+KCie+K/xzLOZfDo0SPlDNoL5s1F8eLFUhxXokRxHDh4EGFhYfB7/BiXLl9G5UoV1X5OuVmA30OUKlNeVuCFYiWdEBLom+FxBgaG+HnaapRxrZTu+3wf3sWzp8GoXqeZ2sqc2z148gLu9rbIaxyfGVrGLj98nr3OVIvC5J1nMKZVLZgaJ844b2WaBx6D2qNUQessLTclimTFMVs98A+WFfc8JsZy3al4EfgGxT/QS0ufNo3h7lRSue4X8hTF7QpmuI8+fmhRTS251Sf1GRDERGMiBeNjiHxssSRl8l/lW9PCIyJROMnwqCIvUOSJvwsLg7mZWarHRERGonChxGPymebFi5ev5Osendqh17BRaN2jv1zv0LIpKicJHBJcu3VXBgvTGvyYBWeVO0RERKBQoUIq98ZA3Jt372TFPs1jkgRcpqamePHypXyd1jFCl86d8c2gwfiiS1e5LloPtDnIzUoLp42E552rKbbr6xugep34wQUS7peevj7Cw94in1niBIXJGRoZwdrGFqEh6Qd6R/ZtRsNmX7Afx0cQrQJJc/3ld0hPT/YFsMib9u/gFSdvoEQBSzQv74j5hy4pt6d3DFFuEB4VhaIFbVLUCd6GR8Ain2mGxz8OeYoTV29j/eQfPmofkdYEA8uWLUPbtm1RuHDhjzpu2rRpmDhxosq2CRMmYFC3dtA0AwN92dE3KWMjI0RFx8A89VhAVkiNDQ1TvF9Y6bEF5cs64/uB/WTrwJS5i7B9zwF0at1c+f7IqCj8sfgv9O76Baws064k6br4e5P4VFIwMjaWgWVaFXsDfQOVY4z/e39G1q4TfV9cMWzIEBkIzpz1B3bu2oV2bduq4Uxyl16Df0FsTMprenjPRuhB9QmKkZEJYqKj0g0GMiPs3RvcuHQCX/Yf9X99jq4x0NeDkaFq8GRsaIDI2PdpVux9nr7C1sv3sXlIh2wqJVHOIf6+J68TmPz3Nz6jYCAuLg6TVmxG+/o14Whvl+l9lDmKZH9f6P/3SY/W5s6di+vXr3/0cWPHjsWbN29UFrEtJ7AwM8Prt29VtonKulGSyn5y5uaqx0REJr7/8Mmz6N21E6ytLFHcvojsZJy8D8K8ZathW7AAurZvpfbzyU1EhV/8W0kqMjJSPknO7DGREREwTOdeJjh+4jh6dP8SVlZWKGZvjy+7dcXBDPon6CpLKxs5kk/yRWx/9za+hSxBVGQ4DAzTvl+Zde3CMTiVrfR/BxW6xjKvCV6FR6lsi4iJhZFB6n8CFAoFJu06jWFNqsLWIl82lZIo57AwM8Wrd2EpWgvSqxMkWPHvYbwNi8C33dt81D4irQoGRF+BKVOmyLzqjyFSgkR6UdIlp6QJuTg54p7nQ+V6SOhTOYpNWilC8pjSjrib5BjRCbiATXwOrUIRh9dJKqMvX73Gh7jEKSt27DuEKzduYfyo4Ux3yEAZpzK47+mpXH/y5AliM7g3Zco44f79xGO8vX1QwCaxyTctcXEKvH7zWuW+xX3IzVONqJ9DaVd4e91Srj8LDcL797EwU0MF/vLZw6hSs9H//Tm6xs2+IG4FPFWuB756K/sDiCAhNSFvwnD9cagcRajO1L/lIrZ1Xrwd+27G970hys3cHIrj9sPEgT2Cnr5ArGhJM0u/VeDUtbvwOHASM0f0UfY3yMw+Ik36pFpoQkfM0qVLY8yYMZg0aZJy0VbubmURHhmJfUfih/hct/VfOQKQSFERaT5iboDk6n9WA8fOnIO3n79sFRBpQNUrxeeXu7u6YNnajTh84gy27d6PlRu2oHa1Ksp+An+uWoex3w5BHpM88lgxLwGlrnz5crIPwKFDh+X6ps1b5Cg/BgYGMiAVczUkV6d2bZw8dQq+vn6yFUGk+lSpUjnDn1XOzQ2rVq/BsePH8e/OXVi7bh1q1qyRJeeVW5Vxq4zIiHCcPrpLru/dtgqu7tWVI/9EhL9DXCr3LCMizcjr7jU4l4v/HlHmVSlRGGHRMfj3WvwIQitP3kANx6IyFUL0G0j6oEKwNc+HfSO7YfOQjsqloHk+LOrZHA1cSmjoLIiyTyWXUgiPjMauU/F9ZVbvPoLqbmXi+6uFR6b4zgi+QaH45c91GN2rAwrZWCEiKlqZOpzePvo47ECcQ/oMiDHcnZ2d5fL0aeLTpsxOYJMTiQm/fhz6DSbNXoilazygp6+H+VPGy32iE/CKudPlMKBJlXYogU6tW2DgDz/D2NgI9oULo32L+I6TIwcPwNylqzB/+Wq8f/8eDWrXkqlCgggaRKvDDxOmKj+rYrmymD91Qraes7YQlf7vvh2B6TNmYsWqVfLf2cwZ8ROFiY6+ixcugKOjo8oxpUqVQvt2bTHi229l/4KiRYqgdauM07GGDx+GRYsXy6FHxX2rV7cuunfrlmXnlhuJUYH6DB2HZXN+xta/50FPTx8/TvlLuX/YVw3w25wNKO7g/FGf+8jrFkzNLGBrx0mXPpahgT5+a1cPY7Yek0/7xXcoYaKwur+vlZV9l8I2Ku9P2uFYbtPXQyGLfDA1+f/TvejT7TX6uO8NfXqd4NcBXfDL4vWYv3E39PX0sOyXIXJfw0G/wGPKD3BONhP0P8fPywnFflu2US5C4QLW2D13XLr7iDRNTyGSQzXsiefH9z/IKi9evcYDbx+4lnGCpUXao84k5ecfiGcvX6Kim2uKDkfays6lEny9c1Y6wMuXL/Hw0SM5zKhIMcuMx/7+ePH8hWxdSN4JWVs5OJbG2Xsfl6KnCW9ePZeTiDmWKQ8zCyvogtquZoja8gdyqufvInAv+Dnci9nK4UF1RZ4uo1iJzqFaxXrh3aW9yKmev34LT79AOcyolblu9Z8xr54z+zNq8u9fbde005O1mVprrs+ePUPBgto9bq6NtRVqVc04nSQpMaeAWChr5c+fHzWqV/+oY0oULy4Xyn6W1gVQoWpdTReDkihgbop6zvw+EGVWASsL1KnIyfUod/ukYMDT0xM//PCDnHgsIV9bNDCImV0zM3wjEREREdHH4tCiOaQDcZ8+feDq6ooGDRqgSpUqWLx4sZwddvr0+DxuIiIiIiLKpcHA7du3MXr0aAwcOBABAQFo0aIFVqxYgTVr1qi/hERERERElHPShMQoQqLyP2rUKHh7e+PFixewtbWFr6+v+ktIRERERCTmA9L4sDeZI7JlDhw4kOH7nj9/jj179qBkSdURK3N8MLBw4UJ07twZAwYMQL9+/WRwIIaqa9u2rfpLSERERESkRezs7HDiRPzcVen5999/la9PnjyJQYMGyQF5fv75Z4wcOTLD40Xf3WHDhsHDw0MOid6jRw8sWbIEhpmYLfv/CgZq166NoKAgWQDROtCsWTM5KVTz5s0/5eOIiIiIiHS2A/GzZ8/kQ3UxQE/37t3RrVs3VKpUCQ0bNsywBeL69eu4cOECYmJi0LRpU1SvXh1ff/111vYZCA0NRceOHWFqaiojH9FnYO3atXIceCIiIiIiyjzxZL9IkSIYN24cnJycMH78eKxcuTJTaUYbNmyQA/tUrFhR1slFcPAxPikY6Nu3rxxK9NKlS3jy5AnOnTsnhxQV24mIiIiIcpvo6Gi8fftWZVHXkPo3b96UrQAi7V4QT/evXr2a4XFz585FqVKllOteXl4ymMjyYEBU/ufPny8jEDHJmBheVKyLXCciIiIioqygUOhpbJk2bRosLS1VFrFNHURg4eDgoFy3sLCQ83d9jOPHj+POnTv46quvsj4YEPMLbNmyRWXbpk2bZJ4SEREREVFuM3bsWLx580ZlEdvUQXT4NTExUa6L+btEf9zMCg8Pl/0EJkyYIB/Uf9TPxicICQnBmDFjsGjRItjb28Pf319GLzVr1kSjRo3ke44dO/YpH01ERERElOOYmJioVNjVKX/+/LITcYJ3797B2Ng408eLEYWKFy8uOyB/rE8KBoYMGfIphxERERERUTLVqlWTHYETiE7ARYsWRWb8+eefOHToEK5duwZ9ff3sCQZ69+79KYcREREREVEyYljRoUOH4siRI6hfvz5mzpwph+4X4uLiZJ8C0UchoYNx0n4CYj4CMXFZvnz5EBYWBgMDA+TNmxdZGgwQEREREWU3hZbMQPyxChQoIEcGatmyJczMzGBlZYU1a9bIfSIdX3QufvXqldye1IIFC+SIRp9//rlymwgmMjPhWQIGA0REREREaiRy/sWAO5np+Ltjxw75Wsw+LFoDPD09UbduXRkUCCVLlpRD+qcm4dj/B4MBIiIiItIKcVoyA/Hw4cPl8rFEC0DSIUazwycNLUpERERERNqPLQNEREREpBXE5F+kXmwZICIiIiLSUQwGiIiIiIh0FIMBIiIiIiIdxWCAiIiIiEhHsQMxEREREWmF3DrpmCaxZYCIiIiISEcxGCAiIiIi0lFMEyIiIiIiraDQkhmItQlbBoiIiIiIdBRbBoiIiIhIK8SxA7HasWWAiIiIiEhHsWWAiIiIiLSCQsE+A+rGlgEiIiIiIh3FYICIiIiISEcxGCAiIiIi0lEMBoiIiIiIdBQ7EBMRERGRVlBwaFG1Y8sAEREREZGOYjBARERERKSjmCZERERERFohDpxnQN3YMkBEREREpKPYMkBEREREWoEdiNWPLQNERERERDqKwQARERERkY5iMEBEREREpKMYDBARERER6Sh2ICYiIiIiraBQcGhRddNTKNgvm4iIiIhyvl1XPmjsZ7etaoDcKEe0DESc3qrpIlAypnU7Y+omzX3hKG2/dDPA03tXNF0MSoWta1W8vnFC08WgZKwqNsC7S3s1XQxKhXn1Vthr5KzpYlAqWsV6ISeK4yNstWOfASIiIiIiHcVggIiIiIhIR+WINCEiIiIiooywp6v6sWWAiIiIiEhHsWWAiIiIiLSCAhxaVN3YMkBEREREpKMYDBARERER6SgGA0REREREOorBABERERGRjmIHYiIiIiLSCpyBWP3YMkBEREREpKPYMkBEREREWoGTjqkfWwaIiIiIiHQUgwEiIiIiIh3FNCEiIiIi0gpME1I/tgwQEREREekoBgNERERERDqKwQARERERkY5inwEiIiIi0gpxCj1NFyHXYcsAEREREZGOYjBARERERKSjmCZERERERFqBQ4uqH1sGiIiIiIh0FFsGiIiIiEgrsGVA/dgyQERERESkoxgMEBERERHpKKYJEREREZFWiGOakNqxZYCIiIiISEcxGCAiIiIi0lEMBoiIiIiIdBT7DBARERGRVlAo9DRdhFyHLQNERERERDqKwQARERERkY5imhARERERaQXOQKx+bBkgIiIiItJRbBkgIiIiIq3AScfUj8EAEREREZEaTZ8+HQcOHMjwfc+fP8eePXtQsmRJaG0wEBMTAyMjIygUCujrM+uIiIiIiHSbnZ0dTpw4keH7/v33X+XrkydPYtCgQXj27Bl+/vlnjBw58qN+po+PD8qVK4eIiIiPOu6Tau/v3r3DN998g0KFCsHU1BS3b9+Gvb09rl69+ikfR0RERESks549e4a2bduie/fuOH/+PDw8PHD8+PGP+gwRSERGRn70z/6kYKBv377w8/PD33//jXz58sHS0hLff/89hg4d+ikfR0RERESkszw8PFCkSBGMGzcOTk5OGD9+PFauXJnp49etW4fAwMBP+tmfFAwcOXJEFrB58+YyNUhPTw+9e/fG3bt3P6kQRERERESZGVpUU0t0dDTevn2rsoht6nDz5k00bNhQ1qmF6tWrZzrj5sWLFxg9ejRWrFiRfcGAi4sL1qxZI1+LQovl1KlTcHNz+6RCEBERERHlZNOmTZPZMEkXsU0dRGDh4OCgXLewsEBwcHCmjhV9C7p27YrPPvss+zoQL1y4EC1btsTSpUtl/4EuXbrA398fu3bt+qRCEBERERHlZGPHjk3RqdfExEQtn21oaKjyWXny5MlUR+CjR4/i9OnTsv/uJ//sTzmoWrVqePToEfbu3Svzk0Tn4VatWskIiYiIiEiXGNlYo875bbjweS9EPg7SdHFyNU3OQGxiYqK2yn9y+fPnl52IE4iH7cbGxukeExUVJTsNL1u2TPbhzfahRUXF/8svv0Ru9igoFBNW/4OApy/QoW5VfPdFM2UuV1qW7TqGDUfOIzImBnXKl8Hk/l8gX57EfzhxcXHoO2M5Gld2Q69mdbLhLHK3gpZA6+r6sDYHbngrcOxm5n5LDGiuj0JWiffyhncc9l5WZLiPUvJ5HIBpi/5CYMgTtG7SEEN6d8/we3L83EUsXuOB9+8/YFjfHmhSN75pMyIyEgtWrcf5K9dhbGSEru1a4otWzeS+4b9OwY2791N81ukdHll0ZrmLt38QJi/5G4Ghz9C2UW0M79Epw/sk3PLylsdtnTcpxT7x++ybCbPQsHpl9GjzeRaVPHd7FBCCScs3ISD0Odo3qIER3dpkeF/++ucgNh06hcjoGNSuUBYTB36JfHnzZLiPsi4QqLZzKUwd7DVdFNJi1apVw4YNG5Tr169fR9GiRdM95uLFi/D29kbnzp1VtltZWcm5C+rUqZN1fQZEBBISEoLcLCb2Pb5duA5lSxSBx6+D4RP8FLvOXkv3mH0XbmDfxZtY/F1vbJs4Ar4hz7B63ymV92w7eRlhkdHo3rhWFp9B7megD3Spq48nrxRYdSgOBS314O6QceXG0ACwNgPm7viAP7bHLwevKTLcRynFxMbip99nw7lUSayYNQV+gUHYd0z133xqwcPkuX+id+cOmD1hDFZu3Ab/oPi8yD+Wrkbwk1AsnTERP48YiNWb/sGeI/HjNM/4ZRT2rf9LuYwa1A+Vy7lmy3nmhvv0w8zFcClVAmt+/xm+gSHYc+Jchsfd93mMH2cvQez796nu/+fwKYRFRKJri0ZZUOrcT/ydGTlnJVwc7LFu0vfwCQrF7tOX0z1m/9mrOHD+GhaM/gZbpv8I3+CnWLPnWIb7KOtU9piD4E17NF0MnZqBWFNLVhLDip49e1YO0hMbG4uZM2eiWbNmygcvr1+/lnN6JVWjRg05t8CNGzeUiyD+X7Vq1Uz/7E8KBubOnSsjltzs7J0HCIuIxg9dWqCYrQ2Gdfwc/55Jv1f3k5dvMKlfJ5QrZY/ihWzQtFp5ePonBk1PX7/Fwn8OY0z3VjAStU76vzgWBkyMgMPXFXgdBhy/FYeKpTIOBuysxb0AIqKB6Nj45f2HjPdRSheu3URYRASG9fsKRQsXwsAeXbD3v8p7WkTlvlJ5V7T5vCEcSxRHxxZNcfDEGVlhPX7uAob26YHCtgVRqZwrWjWpjzOX4r93pnnzwDxfPrnky5sXW3cfwIAeqk9DKHXnb9xFeEQkvuvVGfZ2BTG4W3vsOn423WMio6Lx0+yl6NysYar7n718jSWb/sWovt1gyN9nn+TczfsIi4zEyC/bwb5QAQzt3BI7T15M95jQl6/x2zfdUc6xBIoVKoimNSrCyy8ww32UdW4NGge/Res0XQzScgUKFJD1a9EnV8zj5eXlhV9//VXuE/1yra2t8ebNG5VjRL8CMXNx0kUQ/xf7sjRNSIyBOmXKFNSrVw9mZmbIjR4EPEH5UsWQ1yQ+X6uMvR18ghNzuVLTr2V9lXW/J89lUJDgj037UNjGSgYNNx75o2Lp4llUet0gUnmCXiRW1kUlvoBFxscVya8HC1Pgu/b6snXh7mOFDCg+xKW/j1Ly9nsMtzKlkee/HErHksVl60B6Hvk9Ro3KFZTrZZ0csWbLPwgPj5BpQ7YFEr8zYuji1GY2P3n+MmzyW6O8Sxm1nk9u9dAvAG5OpZDnv99nTiXs4ReYfuuuqOAvn/wjAkKeYncqgcPcv7fAroANQl+8kqlE7s6OWVb+3OqBf7CsuCvvS/Ei8A16ku4xfdo0Vln3C3mK4nYFM9xHWSeSAZfO9Bn4GCLnv0GDBhm+Lzw8HDt27JCvRf6/aA3w9PRE3bp1lXVsUblP3iqQlsy+7/8OBkTnYaF06dJyfoGknRbEJAlpEWOxJh+PNas6Yvy/RCpP0YLWynWRw6mvr4e34ZGwyJc3w+MfP3mO49fvYeO4IXL9prc/Dl+5I/sRBD57iRV7T+Izt9L4qUebLD2P3OCLOvooYZtyu2iyu+evSLEtjxEQFZv259lYAAHPFDh1RyHf266WPqo7A+fvK9LdRymJp83iKb7q90Qf78LCYW6WL81jitgm3tB8pnnx/NVrWFqYy0BAtASIVoPIqCicOHcJXdu2SPEZ2/cdROfWKbdT6sIjo1DE1ibFfXobFg6LNO6TkaEhbPNby2AgudsPvHH0wlV8Vqmc7IOw6p99qFnBFaP7dc/S88htwqOiULRgKvclPAIW+UwzPP5xyFOcuHob6yf/8FH7iCjrDR8+XC4fSwwvmnSI0ezwScGAmH3Y2dlZLk+fJv6hyKjTkxiLdeLEiSrbJkyYgB8b57z5CQwN9KGAatO3iZEhomJiMwwGRG7Xb2v+kZ2OHYsWktt2nLqC8qXssWBET3mdOtaripZjZqNb45ooySc36dp/OQ6GqfxLrVZGD0hWRxdP8I0M0w8G9l9RPejMXYX8LFHhT28fpWRgYAAjI9VtJkZGiIqOTjMYiD8m8YaKjsLiIYGoBP009GtMmvsnTl+8ggc+fjJ1qGn92irH+/oHIjAkFHWqV8mak8qFDAz0YZzs172xsRGiYmJggY8fgeLfo2fgVtoBc8YMk7/P2jeqg3bDfkaX5g1RooidGkueuxno66t8FxK/PzEZBgPi78ykFZvRvn5NONrbZXofEZFagoHVq1erdXzWD5dy3vwEosLvHRSqsi08KiZTubHL95zAm/BIOfpQgtBXb1C7fBllwGSX3wrWZqYIePqSwUAGwkVjUioT/IVHAgWtVLcZG8YHBB/1+VEKmOfV++h9BFiYmcHHP0BlW0RklBwvOe1j8uH123eJ74+KVL6/WsXy2LZ8PgKCn2DUpBno1q4V8pmqVoqOnD6H+jWryQouZY645t4BwSnuk3j6/ymevnwlWwUSfp8VKpAfVhZmspWAwUDmWZiZwjvwSYrWgszclxX/HsbbsAh8273NR+0jIkpOrX9Nk46PmhpR8RczqiVdcmqakFvJorjlnVjJCXr2Uo6oYZlBq8DJG55Yf/gs/hjcXdnfQChkbYnomMQROSKiovE2IhK21plIcqdUBb9UoKhNYkXdMl/8CEORMekf17uJPsyT1C/tC+jhTXjG+ygll9KlcNcrPm1QCA59ipj3sTJISPsYR9zxeqhcf+DzGAXzJ6bkmRgbIyA4RFY0u7RpnuL4Y2cvol7Namo9j9zO1bEk7jzwUa4HP32O2Nj3aaYIZUSkD0XHJDa/RURFyZSjgtbJonNKl5tDcdx+6KdcD3r64r/7kn6rwKlrd+Fx4CRmjuij7G+QmX1ERGoLBkTHBjHJmJOTE0qVKiUXkd8kJh/LLSqXKYmwqGjs/G8EoZX7TqJGWUfZrPsuIhIf4lI+fhbDj45dvgU/dm8Nu/yWssIvxnoWmld3x47TV3DxvjeCX7zC7x67ZYuA6JhMn8b/WfxoQgnDidZ21YNfaGLnIrEvtcy1528UaFlVH0XyA+VL6qGGsx6uPlJkuI9SquDmIvsA7D16Uq6v27YTVd3Lyaf278LD8SGVZpoGtarh2Jnz8H7sL59Ob997ENUruaukOIghRQd0/0LZMTlB8JOnCH32HOVcnLLh7HKPimWdZL+BhI7Aa3bsR7XyLvG/z8IjUv19lp6mtath57HTuHz7PkKevcDMFRtRsoid7JhMmVfJpRTCI6Ox69Qlub569xFUdyvz331J/e+Mb1AofvlzHUb36oBCNlby74xIK8poH1FuIb4Wmlpyq09qI+7Tp4/s5VykSBE57mm/fv1k+s/06dORWxgaGGB87/YY+9cWzNt2UD6lXD66v9xXb8RUbBo/FM7FC6sc88+pK7LyP37VdrkIYvSgfTNGoaZbaYzo1BS/r9+F0JdvUKZYYcwa3C1Tk/5Q6kSlf++lOLT/TB+NK+rJ9fXHEr+tozoZ4H/t3QtwTXcewPFfgtYj8iCUaSSiXutVtkWLImZLvGpWvR+lyy4t09ajY7Jq0FJNq8wWa7Qosx6Z6u50+qJqS0dbpaiUslrEol6ViFcqRc7O72/uXSL33Jv0Jvdxvp+Zy32dk3PP/5zfOf/z//3/Z+mGG3Im9/bpNu2xpHfbCBnWJdKkIP0705K9Ry2vn6Ho/WTKuNEyc94iWbxyjURERMobs6aaz3oM+4ssnzdbGiTfHOrMpX5ykvTrmSp/njzN5K0n1K4lf0z9/w2rNmzZKpHlIqV7l453/L3d+/ZLg3pJpvUAxSunv44ZLtPeWCoLVv9TIiMi5e/Tb6Zs/uFPE+Qf6S9Iw7p1fJ5f2xZNZPyQvpK+bI2cPXdeGtRNkDkTxxDPSlAuL4weIFMXrZK/rf1AIiMiZMnUm4NOpIydKqtnTZJGSbffdOhfm7eZ48yMJWvNQ9WOj5MP5k+z/Qyl76MKjQK9CECJRFglGINIRw/Kysoy456OHz9evv76a3OjhKeffloyMzOLvRB5W9dJsDp34ZIc+O9JM8xorJem23BS+ZH+MjsjNAbYr1JRpHacmGFGvaUIhYOpg8rJ2f07JZhkn8+Vg4ezzDCjOiqQL7KOn5Bz2eelZdPf3dGJMlTVbPKg5O6xv89CIGXnXpADR45J8wbJElM1PIeFLkpsy85yacdHEqzO5V6U/xw9YYYZja1astStUFW1TU9OooNUz2sHJRgt2Ri4vz2mq4SlEh2BdRShpUuXyuTJk81tkLOzs6VmzZqmghBu4mOqyiMtCFTB7MpVkUPhfUPsoFc9LlbaPdiqWNMk10kwD5Sd6rEx0uH3zQO9GCgkPjZaOrTkbtpAON1nIOz7DCxYsEAWLlzoThHSykG7du3MrZQBAAAAhFnLwK5duyQxMVFq1Kgh7du3l59+unmX0fT0dHPrZL3TWmrqnSN/AAAAAP5Ay0AAWwZ09KCcnBzzvEmTJpKXl+fuLNapUyfp1auX7djiAAAAAEK0MqBX/uPi4txDi+rwfwAAAABCl8+X8lNSUkzrQPPmNzufPfXUU3JXEcP7LV++3L9LCAAAACCwlYGMjAxZsWKFGTlI04Pq1KkjFStWLJ2lAgAAABA8lYGoqChzTwE1c+ZMSUtLk+jo6NJcNgAAAMCtgA7EwTG06Nq1a82NxwAAAACErhIN/zNw4ED/LwkAAABgwwro2KIREo5K1DIAAAAAIPRRGQAAAAAciruEAQAAICRwB2L/o2UAAAAAcChaBgAAABASCgoCvQThh5YBAAAAwKGoDAAAAAAORWUAAAAAcCgqAwAAAIBD0YEYAAAAIYGhRf2PlgEAAADAoWgZAAAAQEgooGXA72gZAAAAAByKygAAAADgUKQJAQAAICTQgdj/aBkAAAAAHIrKAAAAAOBQVAYAAAAAh6LPAAAAAEKCFdCxRSMkHNEyAAAAADgUlQEAAADAoUgTAgAAQEjgDsT+R8sAAAAA4FC0DAAAACAkcNMx/6NlAAAAAHAoKgMAAACAQ5EmBAAAgJBQQA9iv6NlAAAAAHAoKgMAAACAQ1EZAAAAAByKPgMAAAAICQwt6n+0DAAAAAAORWUAAAAAcCjShAAAABASSBPyP1oGAAAAAIeKsCzqWP6Qn58vc+bMkbS0NLn77rsDvTi4BWUTnCiX4EXZBC/KJjhRLghlVAb85OLFixITEyMXLlyQ6OjoQC8ObkHZBCfKJXhRNsGLsglOlAtCGWlCAAAAgENRGQAAAAAcisoAAAAA4FBUBvxEOwxNnz6djkNBiLIJTpRL8KJsghdlE5woF4QyOhADAAAADkXLAAAAAOBQVAYAAAAAh6IyAAAAADgUlYFSsGXLFqlbt26pzHvFihXSuXPnUpl3ODh69KhEREQEejGAoFZUjJoxY4aMHDkyYMuE0DoWOcWlS5ekV69eUrlyZalZs6Z88803gV4kwO/K+3+W6NChg3z33XeBXgwAKBIxCvD9AtypU6fk8OHDkpubK9WqVQv0IgF+R2WgFJQvX57bkQMIWsQowDfZ2dnSvHlzqV27tnkA4cgRaUKbNm2SJk2amGa+du3ayaFDh+5ItymcXqKf6XfmzZsnSUlJ8vHHH5v3X375ZRk2bJj7e/v27ZPq1avL9evXbZtmvU23YcMGE3BiY2Nl9OjRkp+f7/7uSy+9ZJonGzZsKN9++62EO13vbdq0kT59+khMTIykpqaaKzNKf//DDz8sUVFR0r59e/n+++99mueXX34prVq1MtuAznv//v2224fL2rVrJTk5WapUqSLdunWTc+fOlcIvDl+e1rtelW7WrJm5yjZhwgRp3LixLFiwwOu+AN/Zbdee0kfOnz8vXbp0MRWFoUOHypUrV7zuC5paNGjQIHnggQdMeY4bN06uXbtWRr8yvOKbXZzS9aypXKtWrZJGjRrJ4sWL3Z95i4vvv/++OY5p+SxcuLAMf23oysjIMOcEM2fOlJUrV5rnGqd+SzlpilHbtm1Nufft21cuXLgQkN8GOLIyoCfhTz75pBw8eNAcHKdNm+bTdEuWLJFPP/3U/K87sHr88cdl48aN4ro9w/r1601Q1yttduym04O0Pn/22WdNsNixY4e89tpr7iA+f/58effdd81BRAOME+h60IPbnj17zE1cxo4dawKnHjh79uxpyrJ169bmhMWbgoIC6devnwm+R44ckY4dO8rkyZO9bh+aKzpixAiZM2eOObhqWb3++uul+rvDid161/IcPHiwbN68WZYtW2YeWg52+wJKP+5pvBk+fLjs3LnTTKcnQr7sCzrdrFmzTCXjk08+4YSzBPHNW5xSrnU7d+5ceeyxx8x73uKiXtlOT083F7RefPFFmTRpkly9erXMf3Oo0WO2Vo6nTJliYpU+13IraTlpilH37t3NQy+GXLx40ZQFEBQsB6hbt641e/Zs6/Lly9aNGzes/Px86+2337Y6derk/k5WVpaepbtf62fNmjUz3y2sadOm1o4dO8zzLl26WB9++OFtn2/evNlKSkryebpZs2ZZbdq0cX9v8eLFVuvWrc3zkSNHWuPGjXN/9vzzz9+23OFIyyYhIcEqKCgwr3fv3m2VK1fOWrlypdWoUSP393JycqyMjIzbpi1cjkrL/NSpU9bVq1fN+h81apRVr1492+1D5eXlWZUqVTJ/V6e9fv269euvv5byrw8fdutd1+vBgwfN84ceeshas2aN130BxeNpu/YUo6ZPn261bdvW/XrdunVWcnKy131hxIgR1tChQ93TzZs3L+xjVGnENy0fuzil67lGjRpWbm7ubfNbvXq1x7io5azxMDMz07zWv6Gvjx49Wka/NvTpfqHr3tfjiadyWrVqlVWrVi13ua9fv958DwgGjmgZ0KvpegXy3nvvlZSUFJOiU1heXt4d7+nVmrvuuuuO9/WqgF7Z1yb0vXv3yqOPPurTcnia7sSJE6aZV9Mi9KFXGY4dO2Y+0+bjxMRE9zzuu+8+cYKEhAR32paW240bN8y60DQFl7i4OBk4cKDXeUVGRprWFZ2PpjDoFR6dn7fto1KlSqap+M033zRpWnqF5/jx46Xye8OR3XqvX7++bNu2TXJycuTHH380V6697Qvwf9wr7Nb9S+OOK33F275Qp04d93P9e2fOnPH77wn3+Kb7h12cUto6oykmt9JysIuL+rpFixbmuet45mqhRvF5O554KieNbT///LMpD41tAwYMMK9ppUEwCPvKgJ7ka16+pvtojquOoqE5fRqItbnPZdeuXXdMq7mxnpoP9aT+s88+k65duxZZYSjOdHpg6N27t2ky1kdmZqZZXqUH3pMnT7rn4ZQTI/2drgOWHuw0LUHXk/btcLl8+bLJOz99+rTtvDR1YenSpSavU9NORo0a5XX7UHqies8998gXX3xhTm7i4+PlueeeK7XfHG48rXctVz35f+aZZ6RWrVryxBNPyP333+91X4Dv7LZrO7ee4Gvc0e3fl33h1v1S56HliuLFN62seYpTdsckrYjZxUU6ivuX3fHErpw0tmm/mltjm174qFChQhktOeDgyoAeELWz2+rVq+Xs2bMmAOt7WqvXnVnz9rR2/uqrr/o8T+3cqHmaOk+92v9bp9POd1u3bjVXSDV/VDtSaq6v0itw+v2vvvpKtm/fLm+99ZY4gZ6IaH5yVlaWyVvWPHId61lPSvR9vcqiOcp6RcZ1wuKJ5ju7cja149fEiRPdB2JP24fS19qRXDu06t91fR++8bTeNa9Zt3d9T4fr0076vuwL8J3ddm1H44y2KGjfDc0z1/3Ol33hvffeMznpmgu9aNEic+EDxYtvrtbpouKUHe0rUJK4iJKxO554KyetBGoFQlvatB+g9vWglQZBwXIAzX1t3LixVbFiRdMP4PPPPzd5f4MHD7YSExNNTrLmLBfuM6C5nZ6kpaVZVapUMbm0hXnqM2A3neYP6rJVrlzZSklJsX744QfzvuYXTp061YqPjzd5oaNHjw77fFxd75pH3qdPH7M+unXrZnI0Xfm1+llUVJRZDwcOHPDaZ0Bzm4cMGWLWu67juXPnWuXLl7dOnz7tcftwWbRokcm91s80n3rfvn1lsg7Cgd16b9mypVWtWjWTK63rdsyYMV73BRSP3Xbtqc9Aamqq1bFjRys6Otrq37//bXnPnvYFzZEePny4+RsxMTHW+PHj6VtTgvjmLU7petYyKoqnuFhUOWt81DiJkvUZ+C3lpH0MtE+Ulrued2zfvr3MfgdgJ0L/CXSFBLiVjpqkD22ORfjRkYPeeecd09SuQ/Pp1eQePXqY9BNSGkKPph/pMKU6nCK8I74BCDbcdAxAmdLOrGvWrDF5zb/88osZ//yVV16hIgAAQADQMgAAAAA4VNh3IAYAAABQNCoDAAAAgENRGQAAAAAcisoAAAAA4FBUBgAAAACHojIAAAAAOBSVAQAAAMChqAwAAAAADkVlAAAAABBn+h/rItdxhfANeAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "与生存率的相关性排序:\n",
      "survived    1.000000\n",
      "pclass     -0.338481\n",
      "fare        0.257307\n",
      "parch       0.081629\n",
      "age        -0.064910\n",
      "sibsp      -0.035322\n",
      "Name: survived, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "\"\"\" \n",
    "使用 corr() 方法对 numeric_columns 中的数值列计算得到的 correlation_matrix（相关系数矩阵），\n",
    "可以分析泰坦尼克号数据中这些数值变量之间的线性相关关系\n",
    "这里可以分析出来，生存率和船舱等级负相关，相关性值为 -0.34，和传票价格正相关，相关性值为 0.26\n",
    "\"\"\"\n",
    "# 数值型变量的相关性分析\n",
    "numeric_columns = ['survived', 'pclass', 'age', 'sibsp', 'parch', 'fare']\n",
    "correlation_matrix = df[numeric_columns].corr()\n",
    "\n",
    "# 相关性热力图\n",
    "plt.figure(figsize=(10, 8))\n",
    "sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', center=0,\n",
    "            square=True, linewidths=0.5)\n",
    "plt.title('数值变量相关性热力图')\n",
    "plt.show()\n",
    "\n",
    "# 重点关注与生存率的相关性\n",
    "survival_correlations = correlation_matrix['survived'].sort_values(key=abs, ascending=False)\n",
    "print(\"与生存率的相关性排序:\")\n",
    "print(survival_correlations)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "34b1c13b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ASUS\\AppData\\Local\\Temp\\ipykernel_9568\\3604384760.py:7: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  age_group_survival = df.groupby('age_group')['survived'].agg(['count', 'mean'])\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "按年龄组分组的生存率:\n",
      "            人数       生存率\n",
      "age_group               \n",
      "儿童          69  0.579710\n",
      "青少年         70  0.428571\n",
      "青年         535  0.353271\n",
      "中年         195  0.400000\n",
      "老年          22  0.227273\n"
     ]
    },
    {
     "data": {
      "image/png": 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KhCUAAIBa7oYX7VEff/zxfL2J1q5WDgAAbP4aGhrS8uXL0/bbb99mo6KaCUsRlKLFKwAAQIgLn++4444p1XpYqlzBPL4hW2+9dbV3BwAAqJJly5blQkolI6RaD0uVoXcRlIQlAACgbh3TczR4AAAAKBCWAAAACoQlAACAAmEJAACgQFgCAAAoEJYAAAAKhCUAAIACYQkAAKBAWAIAACgQlgAAAAqEJQAAgAJhCQAAoEBYAgAA6Ehhad68eWnYsGGpT58+ady4camhoaHNx5900kmprq5ureXRRx/daPsMAADUjqqEpdWrV6fRo0enoUOHplmzZqX58+enadOmtbnNNddckxYvXty4/OAHP0hvfOMb04ABAzbafgMAALWjKmFpxowZaenSpWnSpElp0KBBacKECWnKlCltbtOjR4/Uu3fvxuWKK65In//851Pnzp1bDWTLli1rtgAAAHTosDR37tw0fPjwHIDC4MGDc3WpvX7/+9+nhQsXpuOOO67Vx0ycODH16tWrcVGBAgAAOnxYiirPwIEDG9dj7lFUiGJ4XXtcffXV6fTTT0+dOrW+++PHj8/Vq8qyaNGi9bLvtSLmkK1YsaJxWdecMgAA2NzUV+VF6+tTt27dmt3WvXv3tGrVqtzwoS3PPPNMmj59erryyivbfFw8f8vXoP1WrlyZjjzyyMb1+J737NmzqvsEAACbfWWpb9++6emnn2522/Lly1PXrl3Xue13v/vddOCBB64zVAEAAGxyYSlahs+cObNxPeYfRUOGCFHrcuutt6ajjz56A+8hAABQ66oSlkaMGJHnLU2dOjWvRze8kSNH5nlLS5YsSWvWrClu9+yzz6Zf/OIX6eCDD97IewwAANSaqs1Zmjx5cho7dmy+IG00arj33nvzfTG8bs6cOWnIkCFrbXf//ffn+3feeecq7DUAAFBLqhKWwpgxY9KCBQvS7Nmzcxvxfv365dvb6rp26KGHpieffHIj7iUAAFCrqhaWQv/+/dOoUaOquQsAAAAdZ84SAABARycsAQAAFAhLAAAABcISAABAgbAEAABQICwBAAAUCEsAAAAFwhIAAECBsAQAAFAgLAEAABQISwAAAAXCEgAAQIGwBAAAUCAsAQAAFAhLAAAABcISAABAgbAEAABQICwBAAAUCEsAAAAFwhIAAECBsAQAAFAgLAEAABQISwAAAAXCEgAAQIGwBAAAUCAsAQAAFAhLAAAABcISAABAgbAEAABQICwBAAAUCEsAAAAFwhIAAECBsAQAAFAgLAEAABQISwAAAAXCEgAAQIGwBAAAUCAsAQAAFAhLAAAABcISAABAgbAEAABQICwBAAAUCEsAAAAFwhIAAECBsAQAAFAgLAEAABQISwAAAAXCEgAAQIGwBAAAUCAsAQAAFAhLAAAAHSkszZs3Lw0bNiz16dMnjRs3LjU0NLRru5deeintv//+6fLLL9/g+wgAANSuqoSl1atXp9GjR6ehQ4emWbNmpfnz56dp06a1a9trr702LV26NH384x/f4PsJAADUrqqEpRkzZuTAM2nSpDRo0KA0YcKENGXKlHVu9/jjj6fPfOYz6eqrr05dunTZKPsKAADUpqqEpblz56bhw4enHj165PXBgwfn6tK6nH322el1r3tdWrRoUbr//vvXWb1atmxZswUAAKBDh6UILgMHDmxcr6urS507d06LFy9udZuZM2em73znO2nHHXdMCxYsSCeeeGI688wzW338xIkTU69evRqXAQMGrPf3AQAAbL6qEpbq6+tTt27dmt3WvXv3tGrVqla3+cY3vpH23XffdPfdd6eLLroo/exnP0vXXHNNeuihh4qPHz9+fB7qV1miGgUAANChw1Lfvn3T008/3ey25cuXp65du7a6zT/+8Y/07ne/O1ehQlSKtt1221xlKokwtvXWWzdbAAAAOnRYipbhMayuYuHChXmOUYSo1sTwu2effbZxfcWKFemZZ55JO+ywwwbfXwAAoPZUJSyNGDEiz1uaOnVqXo9ueCNHjszzlpYsWZLWrFmz1jZjx47NQ/F++tOfpr///e/pjDPOSG9+85tzcwgAAID1rb4qL1pfnyZPnpwDUFyQtlOnTunee+/N98VFaufMmZOGDBnSbJvDDjssXXLJJen000/P84/i/ttuu61xWB4AAMAmH5bCmDFj8nyj2bNn5zbi/fr1y7c3NDS0us2HP/zhvAAAAGy2YSn0798/jRo1qpq7AAAA0HHmLAEAAHR0whIAAECBsAQAAFAgLAEAABQISwAAAB2tG96mZOi4G1ItqXvx+dSryfrB59+cGuq7plox+9ITqr0LAABUmcoSAABAgbAEAABQICwBAAAUCEsAAAAFwhIAAECBsAQAAFAgLAEAABQISwAAAAXCEgAAQIGwBAAAUCAsAQAAFAhLAAAABcISAABAgbAEAABQICwBAAAUCEsAAAAFwhIAAECBsAQAAFAgLAEAABQISwAAAAXCEgAAQIGwBAAAUCAsAQAAFAhLAAAABcISAABAgbAEAABQICwBAAAUCEsAAAAFwhIAAEBBfelGgI2loaEhrVy5snF9yy23THV1dVXdJwCAICwBVRVB6cgjj2xcnz59eurZs2dV9wkAIBiGBwAAUCAsAQAAFAhLAAAABcISAABAgbAEAABQICwBAAAUCEsAAAAFwhIAAECBsAQAAFAgLAEAABQISwAAAAXCEgAAQIGwBAAAUCAsAQAAdKSwNG/evDRs2LDUp0+fNG7cuNTQ0LDObQYPHpzq6uoal1NOOWWj7CsAAFB7qhKWVq9enUaPHp2GDh2aZs2alebPn5+mTZvW5jarVq1KCxYsSE899VRavHhxXq6++uqNts8AAEBtqUpYmjFjRlq6dGmaNGlSGjRoUJowYUKaMmVKm9vMmTMnV5a23Xbb1Lt377xsscUWG22fAQCA2lKVsDR37tw0fPjw1KNHj7weISiqS2353e9+l/7xj380hqXTTz89V6haE/ctW7as2QIAANChw1IEl4EDBzaux/yjzp0756F1rXnooYfSAQcckO677750zz33pB//+MfpiiuuaPXxEydOTL169WpcBgwYsN7fBwAAsPmqSliqr69P3bp1a3Zb9+7d87yk1lx77bXppptuSrvsskvad9990wUXXJBuu+22Vh8/fvz4PNSvsixatGi9vgcAAGDzVl+NF+3bt2/uhtfU8uXLU9euXdv9HK95zWvSP//5z1bvjzDWMpABAAB06MpStAyfOXNm4/rChQvzHKMIUa3Zb7/9mlWHYvvXve51G3xfAQCA2lSVsDRixIg8b2nq1Kl5PbrhjRw5Ms9bWrJkSVqzZs1a2+y+++7ptNNOS7/97W/T9ddfny6//PLc5AEAAGCzGYYXc5YmT56cxo4dmy9I26lTp3Tvvffm++IitdEmfMiQIc22ueyyy9KHPvShdMghh+QheJdeemk68cQTq7H7AABADahKWApjxozJF5mdPXt2biPer1+/fHtDQ0Px8dEu/I477tjIewkAANSqqoWl0L9//zRq1Khq7gIAAEDHmbMEAADQ0QlLAAAABcISAABAgbAEAABQICwBAAAUCEsAAAAFwhIAAECBsAQAANDRLkpLx9XQuUtaOnhss3UAAKglwhJldXWpob5rtfcCAACqxjA8AACAApUlAID1qKGhIa1cubJxfcstt0x1dXVV3SfglRGWAADWowhKRx55ZOP69OnTU8+ePau6T8ArYxgeAABAgbAEAABQICwBAAAUCEsAAAAFwhIAAECBsAQAAFAgLAEAABQISwAAAAXCEgAAQIGwBAAAUCAsAQAAFAhLAAAABcISAABAgbAEAABQICwBAAAUCEsAAAAFwhIAAECBsAQAAFAgLAEAABQISwAAAAXCEgAAQIGwBAAAUCAsAQAAFAhLAAAABfWlG4HqGTruhlRL6l58PvVqsn7w+TenhvquVdyjjWv2pSdUexcAgFaoLAEAABQISwAAAAXCEgAAQIGwBAAAUCAsAQAAFAhLAAAABcISAABAgbAEAABQICwBAAAU1JduBAAAOraGhoa0cuXKxvUtt9wy1dXVVXWfNjfCEgAAbIIiKB155JGN69OnT089e/as6j5tbgzDAwAAKBCWAAAAOlJYmjdvXho2bFjq06dPGjduXB5z2V5LlixJ2223XXr00Uc36D4CAAC1qyphafXq1Wn06NFp6NChadasWWn+/Plp2rRp7d4+wtWTTz65QfcRAACobVUJSzNmzEhLly5NkyZNSoMGDUoTJkxIU6ZMade2v/zlL9Odd96Z+vXrt8H3EwAAqF1VCUtz585Nw4cPTz169MjrgwcPztWl9lSkTjvttHTVVVets9NHPHbZsmXNFgBqVwz3XrFiRePycoZ/A1CbqtI6PILLwIEDG9ejH3znzp3T4sWL8xym1kQF6k1velM69thj07nnntvma0ycODFdeOGF63W/Adh0abFbPUPH3ZBqSd2Lz6deTdYPPv/m1FDfNdWK2ZeeUO1dgE27slRfX5+6devW7Lbu3bunVatWtbrNn//853Tttdemr33ta+16jfHjx+ehfpVl0aJFr3q/AQCA2lGVylLfvn1zN7ymli9fnrp2LX/qEkMlTj311PTFL34xbb/99u16jQhjLQMZAABAh64sRcvwmTNnNq4vXLgwzzGKEFXy2GOPpfvuuy93wevdu3de4raY6/Ttb397I+45AABQK6pSWRoxYkSetzR16tT0oQ99KM9FGjlyZJ63FNdQ2mqrrfLXFTvssEMOVE0dcMAB6eabb05DhgypwjsAAAA2d+2uLP3oRz/K/zbtHhQB5oUXXshzjfbZZ5+XNWdp8uTJ6cwzz0zbbLNNnmR7ySWX5PuiwcMDDzyw1uNf//rXN1vith133NHkXAAAoLph6aSTTkpz5szJVaEISGvWrEnvec970h133JHnBsUwupdjzJgxacGCBen666/PzRt22223xjDWnmrRo48+mkMTAABAVYfhxUVg99hjj9xgIVp3v/Od70y77LJLet/73pfvbzpsrr369++fRo0a9bK3AwAA6DCVpS222CJ16dIlfetb30onn3xyGjt2bBo9enS6+OKL8/0u7gcAANR0N7yPfexjaa+99kp33313OvHEE9PWW2+9YfYMAACgo4el2bNn5zlJL730Ug5HRxxxRJ5j9IlPfCLtvPPO6W9/+1uexxQNH2L+EQAAwGY/Z+nBBx/MbboHDBiQLwp73nnnpeeeey6dddZZeb7Se9/73jw8b8WKFbni9Pzzz6eVK1dunL0HAACoVmUpKkhPPPFE6t69ew5OBx98cHryySfTIYcckq644op0+eWXp8WLF+fmD/GvoAQAANREWKqrq0u9e/fOYemWW25JV199dbrnnntyiDr00EPTzJkzN86eAgAAdMTW4ZVud9EufNq0aWn33XfPlaRo8lAJVQAAADXXDW/p0qVp+fLl6fDDD09z585N22yzTW4b/sgjj+T7tQ4HAABqMiz16NEjV4+OPPLI9LnPfS716dMnd8OLRg/R1CGaPgAAANRcWPrjH/+YevbsmS688MLG20477bT0/e9/P3993HHHbZg9BAAA2BQuSttU165dc7Up/o1qEwAAQE2FpZiPNHHixMb1aCUeYvhdVJsAAABqMixF9ajp8LvXve516Zvf/GauKG2xxRYbcv8AAAA69jC8uM5SRZcuXdKECRPS6tWrU7du3TbUvgEAAHT86yx16tSpWWe8j3zkI+mII45IK1asSKeeempjoIpGD/vvv/+G2VsAAICOVllqeh2lGJZ3xhlnpEMPPTTV19ent7zlLXmJStPZZ5+9ofYVAACg44SlF198sXh7hKTzzjsvV5M++tGP5uVjH/tYGjBgwIbYTwAAgI41DO+YY47JVaOmYuhdxZo1axq/3mOPPdLtt9++vvcRAACg41WWjj766PTb3/42B6SoHD3++OPpe9/7XmPVqWlwAgAAqJmw9MEPfjDNmDEjN3XYcsst0+DBg9O8efNyRSmG4v3mN79p9viXXnppQ+4vAABAx+uGd/HFF6exY8emt771rencc8/NjR6i8UP8WwlKsd50aB4AAMBm3Q3v2Wefzf/G/KWnnnoqjRgxIh1++OFp1apV6fnnn89LDMurPA4AAGCzryxFxej9739/4/o222yTvv/976dDDjkkXXPNNemss85qvK9r164bZk8BWK+Gjrsh1ZK6F59PvZqsH3z+zamhvnb+Zs2+9IRq7wLA5hmWYgjedddd1+y2aBk+ffr01LNnzw21bwAAAB1/GF7Ja17zmtz4oeKFF15Ihx122PrYLwAAgE0nLC1ZsiTtueeerXa+i+54M2fOXH97BwAAsCmEpS5duqRHHnmksYoU69HYoSK64sVtAAAANRWWonJUCUOVf1s2dKi0EQcAAKjZOUudO3def3sCAACwqXXDGz9+fNpiiy3ydZRi2N1FF13UOGep8jUAAEDNhaUFCxakbt265XAUy1//+tfG+5p+DQAAUFNh6dZbb83/Pvfcc+muu+5KN954Y16/5ZZbGr+u6Nu374bYTwAAgI47Z0nzBgAAoFa0KywtWrQo7b333rmqVBGtw2MOU8trLQEAANRMWIpQdMABB6RPfvKT6dlnn01f/epXc5UpLkDbqdP/e4qGhoa0Zs2aDbm/AAAAHScsDRw4MH35y19Of/vb39J1112XLr744nT55Zenfffdt9njolNe0/AEAACwWTd4aHpdpQ9+8INp9OjR6amnnlrr/uiYt3jx4vW5fwAAm5SGzl3S0sFjm60DNRCWKnr37p0XAABaqKtLDfVdq70XwHpgzBwAAECBsAQAAFAgLAEAABQISwAAAAXCEgAAQIGwBAAAUCAsAQAAFAhLAAAABcISAABAgbAEAABQICwBAAAU1JduBIDNTUPnLmnp4LHN1gGgLcISALWhri411Het9l4AsAnZ5IbhPfbYY2nWrFnp+eefr/auAAAAm7GqhaV58+alYcOGpT59+qRx48alhoaGdW5zzjnnpL322iu9//3vTwMHDkx/+ctfNsq+AgAAtacqYWn16tVp9OjRaejQoblKNH/+/DRt2rQ2t7n33nvT3Xffnf72t7+lhx9+OL3jHe9IF1988UbbZwAAoLZUJSzNmDEjLV26NE2aNCkNGjQoTZgwIU2ZMqXNbbp165a+8Y1vpK233jqvv/Wtb03/+c9/2gxky5Yta7YAAAB06LA0d+7cNHz48NSjR4+8Pnjw4Fxdast+++2XDjrooPz1v//973Tdddelo446qtXHT5w4MfXq1atxGTBgwHp+FwAAwOasKmEpqjwx56iirq4ude7cOS1evHid20Z1aaeddkr9+/dPJ598cquPGz9+fK5eVZZFixatt/0HAAA2f1VpHV5fX5+H1TXVvXv3tGrVqtzwoS0nnHBC2m677dLpp5+evvKVr6Qzzzyz+Lh4/pavAXQ8rn0DAHRUVaks9e3bNz399NPNblu+fHnq2nXd17+IAHTEEUekiy66aJ3znIBN59o3lSXWAQBqNixFy/CZM2c2ri9cuDA3ZIgQ1Zorr7wyffvb325cj2AVQ/cAAAA2m7A0YsSIPG9p6tSpeT264Y0cOTKHnyVLlqQ1a9astc3OO++czj777PTzn/88PfTQQ+nSSy9NxxxzTBX2HgAAqAVVm7M0efLkNHbs2HxB2k6dOuXrKIWYszRnzpw0ZMiQZtvEdZnOO++8dPzxx6cXXnghnXLKKXlbAACAzSYshTFjxqQFCxak2bNn5zbi/fr1y7c3NDS0us0555yTFwAAgM02LIVo/z1q1Khq7gIAAEDHmbMEAADQ0QlLAAAABcISAABAgbAEAABQICwBAAAUCEsAAAAFwhIAAECBsAQAAFAgLAEAABQISwAAAAXCEgAAQIGwBAAAUCAsAQAAFAhLAAAABcISAABAgbAEAABQICwBAAAUCEsAAAAFwhIAAECBsAQAAFAgLAEAABQISwAAAAXCEgAAQIGwBAAAUCAsAQAAFAhLAAAABcISAABAgbAEAABQICwBAAAUCEsAAAAFwhIAAECBsAQAAFAgLAEAABQISwAAAAXCEgAAQIGwBAAAUCAsAQAAFNSXbgQAgE3N0HE3pFpS9+LzqVeT9YPPvzk11HdNtWL2pSds8NdQWQIAACgQlgAAAAqEJQAAgAJhCQAAoEBYAgAAKBCWAAAACoQlAACAAmEJAACgQFgCAAAoEJYAAAAKhCUAAIACYQkAAKBAWAIAAOhIYWnevHlp2LBhqU+fPmncuHGpoaFhndtceOGFqW/fvqlbt27pqKOOSsuXL98o+woAANSeqoSl1atXp9GjR6ehQ4emWbNmpfnz56dp06a1uc23vvWtvPzwhz9MDz74YPrzn/+cLr744o22zwAAQG2pSliaMWNGWrp0aZo0aVIaNGhQmjBhQpoyZUqb2yxatChdf/31aZ999klveMMb0rHHHpvmzJmz0fYZAACoLfXVeNG5c+em4cOHpx49euT1wYMH5+pSW84777xm6w899FB64xvf2Gb1KpaKZcuWver9BgAAakdVKksRXAYOHNi4XldXlzp37pwWL17cru0ffvjhdMcdd6RTTz211cdMnDgx9erVq3EZMGDAetl3AACgNlQlLNXX1+cmDU117949rVq1ap3bvvTSS+nkk09Op5xyStp9991bfdz48ePzUL/KEsP4AAAAOvQwvOhoF93wmorOdl27dl3ntl/4whfSM888ky699NI2HxdhrGUgAwAA6NCVpWgZPnPmzMb1hQsX5vlFEaLactddd+WmELfffnvjfCcAAIDNJiyNGDEiz1uaOnVqXo9ueCNHjszzlpYsWZLWrFmz1jbRKnzs2LHp6quvzvOPVqxY0a5hewAAAJvUnKXJkyenM888M22zzTZp+vTp6ZJLLsn3xUVqH3jggbW2+frXv55WrlyZTjzxxLTVVlvlZbfddqvC3gMAALWgKnOWwpgxY9KCBQvS7Nmzcxvxfv365dsbGhqKj7/iiivyAgAAsFmHpdC/f/80atSoau4CAABAxxmGBwAA0NEJSwAAAAXCEgAAQIGwBAAAUCAsAQAAFAhLAAAABcISAABAgbAEAABQICwBAAAUCEsAAAAFwhIAAECBsAQAAFAgLAEAABQISwAAAAXCEgAAQIGwBAAAUCAsAQAAFAhLAAAABcISAABAgbAEAABQICwBAAAUCEsAAAAFwhIAAECBsAQAAFAgLAEAABQISwAAAAXCEgAAQIGwBAAAUCAsAQAAFAhLAAAABcISAABAgbAEAABQICwBAAAUCEsAAAAFwhIAAECBsAQAAFAgLAEAABQISwAAAAXCEgAAQIGwBAAAUCAsAQAAFAhLAAAABcISAABAgbAEAABQICwBAAAUCEsAAAAFwhIAAECBsAQAAFAgLAEAABQISwAAAAXCEgAAQEcKS/PmzUvDhg1Lffr0SePGjUsNDQ3t2u7+++9Pu+yyywbfPwAAoLZVJSytXr06jR49Og0dOjTNmjUrzZ8/P02bNm2d282ePTsdddRReXsAAIDNLizNmDEjLV26NE2aNCkNGjQoTZgwIU2ZMqXNbVauXJmOPvrodOaZZ260/QQAAGpXVcLS3Llz0/Dhw1OPHj3y+uDBg3N1qS1dunTJQ/AOPPDAdr1GVJ+WLVvWbAEAAOjQYSmCy8CBAxvX6+rqUufOndPixYtb3aZr165phx12aPdrTJw4MfXq1atxGTBgwKvebwAAoHZUJSzV19enbt26Nbute/fuadWqVevtNcaPH5+H+lWWRYsWrbfnBgAANn/11XjRvn375m54TS1fvjxXj9aXCGMtAxkAAECHrixFy/CZM2c2ri9cuDDPMYoQBQAAULNhacSIEXne0tSpU/N6dMMbOXJknre0ZMmStGbNmmrsFgAAQHWH4cWcpcmTJ6exY8fmC9J26tQp3Xvvvfm+uEjtnDlz0pAhQ6qxawAAANULS2HMmDFpwYIF+UKz0Ua8X79++faGhoY2tzv44IPTo48+upH2EgAAqFVVC0uhf//+adSoUdXcBQAAgI4XlgAAgFemoXOXtHTw2GbrrF/CEgAAbIrq6lJD/fq79A4dpBseAABARycsAQAAFAhLAAAABcISAABAgbAEAABQICwBAAAUCEsAAAAFwhIAAECBsAQAAFAgLAEAABQISwAAAAXCEgAAQIGwBAAAUCAsAQAAFAhLAAAABcISAABAgbAEAABQICwBAAAUCEsAAAAFwhIAAECBsAQAAFAgLAEAABQISwAAAAXCEgAAQIGwBAAAUCAsAQAAFAhLAAAABcISAABAgbAEAABQICwBAAAUCEsAAAAFwhIAAECBsAQAAFAgLAEAABQISwAAAAXCEgAAQIGwBAAAUCAsAQAAFAhLAAAABcISAABAgbAEAABQICwBAAAUCEsAAAAFwhIAAECBsAQAAFAgLAEAABQISwAAAAXCEgAAQIGwBAAA0JHC0rx589KwYcNSnz590rhx41JDQ8M6t7ntttvS6173urT99tunm266aaPsJwAAUJuqEpZWr16dRo8enYYOHZpmzZqV5s+fn6ZNm7bOcHX88cen888/P91zzz3pggsuSA899NBG22cAAKC2VCUszZgxIy1dujRNmjQpDRo0KE2YMCFNmTKlzW0mT56cDjnkkHTKKaekPffcM5155pnpxhtv3Gj7DAAA1Jb6arzo3Llz0/Dhw1OPHj3y+uDBg3N1aV3bvOtd72pc32effdJFF13UZvUqlooIZ2HZsmWvaJ/XrH72FW3HpumVHifrg2OttjjW2Fgca2wsjjU2hWOtsu26pgJVJSzFzg0cOLBxva6uLnXu3DktXrw4z2FqzzZbb711evzxx1t9jYkTJ6YLL7xwrdsHDBjwqvefzV+vqz9S7V2gRjjW2Fgca2wsjjU2pWNt+fLlqVevXh0rLNXX16du3bo1u6179+5p1apVrYallttUHt+a8ePHp3POOadx/aWXXkrPPPNM6tevXw5nrFsE1AiXixYtyuEUNhTHGhuLY42NxbHGxuJYe2WiohRBKRrHtaUqYalv3765YUNTsbNdu3Ztc5unn3663Y+PYNUykPXu3ftV7Xetiv94/vOxMTjW2Fgca2wsjjU2Fsfay9dWRamqDR6iZfjMmTMb1xcuXJjnF0Ugau82c+bMSTvssMMG31cAAKA2VSUsjRgxIpcMp06dmtejG97IkSPzvKUlS5akNWvWrLXNe97znnTzzTenBx54IK1YsSJdddVV6fDDD6/C3gMAALWgKmEp5h9FK/Bo/73NNtuk6dOnp0suuSTfF3OWIhC19Ja3vCWdddZZae+9984VpQhWZ5xxRhX2vnbEMMbPfe5zaw1nhPXNscbG4lhjY3GssbE41jasuoZ19cvbgJ588sk0e/bs3EY8Gi+0R7QY/+c//5kOOuigNucsAQAAbLJhCQAAoKOqyjA8AACAjk5YqmHPP//8Oq9a3Np2N9xwQ1Wv0E1t+/3vf5+H4i5YsKDau8JmIIaER8fV3/3ud9XeFTZjjjM2Fsfa+lWV6yzRMRxyyCFpyJAh6atf/Wpej6YZs2bNSm9961ubPe6FF17IF/WtLBGwJk6cmMPSSSedlC/yG0074vaYXOiiv7UlLvYc8wi7dOnSeNv555+fm7U0vTB0hOzXv/71rV4DIhq3ROOXd73rXet8zSlTpqQtttgiDRo0aD29C2r5OJw2bVp67rnn1vrdB+0R3Xrf8IY3NDaqao3jjFfjwgsvTP/617/SNddc0+z87Nlnn13r76pjbf0SlmrYVlttlZeKCDqlE9k4Mb3uuutyIIq27fGYaMgRrdzjvmi00alTp3ytrLvvvnudV0Jm8/KPf/wj3Xnnnal79+6Ntz3xxBNp6dKl+XgIEaTjF/fYsWNbDUtxLDZ9jtY89thj6frrr0+vfe1r0x577JFvi+OyZ8+ejSfDq1atSrfeemvaf//919O7ZHM9DuPYmTRpUv49VjmxWLlyZerRo0f+4KdyPF1++eXp2GOPrdK7oyOLgL6uhlOOM16JOK+K4yOOm1jiWIsPreN4ifOuOF6++93v5gpS5YNqx9r6p8FDDRs1alTab7/90v/8z//k9TjZnDdvXv7UtSSuf7XLLrukb3zjG7kqFT7xiU/koVBxkkLtOvroo9Nvf/vbxk/141P++EXeu3fvxl/4o0ePTl//+tebbXfxxRen/v375wplBJ+vfOUr6c1vfnN673vfmyueccmA0qe4AwYMSF/+8pfzelybLaoH8QlbBHpq1ys5Dj/5yU/m32Hf+973Gm+Lx0eVPaoF0FQcJ2effXaz2/7973/n3z2V46ziRz/6UXrTm96Uv3ac8UqMGzcuf1gd52cxmifOw+K4ib938Xfz2muvTffcc0/aZ599GrdxrK1/zixqWHw60bSyVBInHh/4wAfyp7UxTC8+mYjrXcUJSIj/uPF1DOeLTyx69eqV7r333o30DugIx9Cjjz6aQ07lmAif+cxnUt++fdOnPvWpxtviWIlf4DvvvHM+VqJCFBekbvoLPUTFKC44feCBB6Zbbrml2bC8u+66K/34xz82V4n1chzOmTMnh/I//OEPVdpzNjURuOPE849//GPjbfE3Mk5CP//5zzfe1nQ4ehxfjjNeiUsvvTQvlQ8XYy5SfFAYx+GIESPybU2DkmNtwxCWaliUYSPctGXfffdNf/3rX/NY2cWLF7f52PgP+/73v3897yUdWXzS1da8oSjztxTjq2N4wIc+9KF8Yeq3v/3tze6Pk4yYaxLPG1WkGOoZw6YWLlyYTjzxxHzftttu2+pJc5wMR8Hcddhqxys5Dh9//PFcwfzwhz+cdtttt+J2cRzF8RSLiz0Smobx9jw2/m46znglYrhca3/HTj755NzA4bTTTsvHT4Sn+NvqWNswhKUatmjRovSa17ymXY/91re+lT796U83lnDjStFDhw5NY8aMyetRAfjVr34lLNWYCNtPP/10uxp7xC/pGDoQVcrx48fncdUXXXRRq4+PYylONqJiELbbbrt01VVXpQMOOCBXOXfdddfGgBRiyF68xosvvpiOOOKIPGab2vBKjsOYsxQT8g877LA8bC9+t8VxFZYvX54rmnF7nFRE5Tx+x8HLbWAUozccZ7xcMYQ4hqRHI6Omx1zMubzyyivzCIz4WxrzMeNvYPytfOSRRxxrG4iwVMNVpegcFUNR2iP+I0a5tzJ5OoJW/MeMMdnhP//5T64CUFvil/g222yTfvKTn+SAEvOPSmJM/xe+8IU8x23+/Pl53ltMSG06x6jSaTHEvzFO+4Mf/GDjJ2tx7MVwl3iu+AMS8+uazlmaO3euOUs16pUch+GYY47J/0bIig974jlCDLOaMWOG8f2spfLhTHsfG7+THGe8XDF8+KmnnmoWnuJv5+mnn57OOOOMPNc8Kk/RuCEablU41jYMZxY1KkJO/OeJTy7aIz6JjdLuTjvtlNfjE/6YkF8ZQvWDH/wgf6JPbYogE8OgYrhmSYyhrnQfi+EBMaSu5Xy5+MUfQwlCfDoW85n22muvxm4+cX97P9mNT87i8RGqqB0v5ziM32kRyuNktvLpa2sq3adcGoH44DC6LDZthFRp8BDtmiviBDaOG8cZr1a0Cz/++OPzPN8bb7wx7b333nm0z2c/+9l05JFHpquvvjo/zrG24QhLNSrmgcQnsO1VGTYVfxRC/Cdruh7zlVobI8vmL345R0CJY6Ikfok3HR9daiwSJxrRbfGyyy7LVcwI4E2vERF/GO644478WnGiUmkbXvmkN4YVtPxDENVPasfLOQ7jWiVxkhHbRHfFgw8+uPFxMWTl3e9+d2NVM46n+DDo5z//eb4eGLUr5oTE0lSpwUNFDJlynPFK/PnPf05f+9rX0vTp0/PxsOeee6ZvfvOb+XiL+eRxHjdy5MjGx/udtuFoHV6DbrrpptxyMoYtNa0sNW0dHv+R4jFx0vqXv/wlT8aPT20rn0DE5OjojFdplRonIfFJxsMPP1y190X1/OY3v8m/mNsa/hQtTuOXfGvimPrIRz6Sj8vokNfei+lpHc76PA6DNru8HG2FpbY4zmhNnEvF77JTTz01t6qPDngxjC4+NIxusZUPfWIYXnxA1Nr1Cysca6+OM4sa8/3vfz8Hn5hY33IIXnziEPdHWTdOOuITjAsuuCAPtfv73//e7LHxyVp8ohEntxDhevjw4Wu1jY/bY8hKdO4plfoj6MS46jjWYqJqnHTccMMNa12vpC3xh6Lyr7BU217pcdhSpVsUtEccKy9nLlPT7RxnlMT1uWJueGU4XeUSLZWmWhUxHO+KK65IDz74YJudGh1rr44zixoSJ6URhI477rh07rnnrnV/VJJi4nO0c47gFP/GpxAx3Ck6qDQtQkbnqbhGSXxKGypdyOL5Y64JtaUy16ilODGNYB1DAJpeCyKuexPXTIphcjH0Lo6bBx54oN0NR5qKdqmhMv6a2vVyj8O2jqnKHDlYlziuXsnx4jijLU3nHR100EE5KN15552Nf+finCuGtMdczHW1tHesvTqG4dWYmPMRV7D3CTwbS6WtaVPR1Scmq0b7+daumQQb+jgE2JRE6/DKtQpjKoTmDBuHsAQAAFDgYzYAAIACYQkAAKBAWAIAACgQlgAAAAqEJQA2GXHx4biWUlwT7tWIjlKrVq1ab/sFwOZJWAJgk3H77benRx99NL3tbW9rV7vwaPj69a9/fa1ry8VFkHffffdm149rer2mfffdN/3pT39ar/sOwKZHWAJgkxDh54tf/GIOOAcccEC+YPagQYPSrrvumr9+85vfnHbaaad02WWX5ceffvrp6Tvf+U6+iOONN96Y7//pT3+a74vK1N57750v1t1SXPgxLvgYYaqib9++aYcddkivf/3r87LNNtuko48+eiO+ewCqwZVJAdgkxJXqu3Tpkp588snGizEOGTIkh6ORI0eu9fgISXHxxqgUnXrqqenzn/98vv3xxx/PVaP7778/HX744emmm25K22+/feN2U6dOTZdffnlasGBBeuKJJ9JBBx2UevbsmX70ox/lwBWmTZuWfvjDH2609w5AdQhLAHR4ixYtSueff3665ZZb2nXV+ghC8+fPT1tssUWenxQBK0LRgQcemM4555xcdYrbJ0yYkA4++OBceYqhd/fdd1/ePm47++yz08KFC3NY6tTJQAyAWiQsAdChPf/88+m9731vnqcUlaCSGJq3Zs2aPHyue/fuacmSJTn81NfX5wpT79690wMPPJB+85vfpIceeihddNFFeRheBKpLLrkkfeADH0i/+93vcvXqscceywHpkUceSbNnz258jUMPPTRXtsKKFSuK1SwANi91DaXZrQDQQcSfqR//+MfpDW94Qw5LsR6BKMRQude+9rWpR48eOSzFXKJf//rX+b4vfelLady4cemEE07IS1Sk/vrXv+bqUsxxOuqoo3KVKdajM148RwSpeJ4rrrgih7OTTjopP1fMU4phdy2H4d18881V/M4AsKEZVwBAhxYh5x3veEfaeeedc5UoAsq8efPysssuu6TJkyfnClEEnUpQCpdeemneNpo4RBOImNu055575iF35513XnrTm96UGz3E14sXL87bxPM9/PDDea5SBKWY7wRA7TIMD4AOLYbWRbUnhsB17tx5nR3z4lpM8dgtt9wyD72LIXnRtCFu22qrrdKoUaMa5yDdeuutac6cOeniiy/O6zFML4b8RVe97bbbLoezaFUe9ttvv8bXjxAVzwPA5s0wPAA6/LWVPv3pT+f5R5U5QxUxDK9///45GIX4kxbh6oYbbkjHH398OuSQQ9I+++yTrrzyyjxE7ytf+UrubBfD7iIQxbC66dOnp6FDhzbORZoyZUqe7xSd9irD/QzDA6hNKksAdGjvec978lLSVuvwqDBFcIrmDTEvqXJh2uis98EPfjANGzYsvetd72oMSiGC1FlnndXYYjyG5EVnPABqk7AEwCYrhufFUhKtwQcPHpw++9nP5oAU7cTjsRGcbrvttnTYYYflClFT1113XR6aF9dhirAVDSWGDx/e6uvHa0R7cgA2T8ISAJusCCvRWrzkwgsvTDvssEMaMWJE7ph32mmnNTZsiLbhM2bMyEP1ok14tBYPMaQvhu5dddVVuQFEzHeK+2LuUsvQFEP+4r6VK1fmIYIAbH7MWQKgZj333HON85IAoCVhCQAAoMB1lgAAAAqEJQAAgAJhCQAAoEBYAgAAKBCWAAAACoQlAACAAmEJAACgQFgCAAAoEJYAAADS2v4/zj2LXtwgBOgAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ASUS\\AppData\\Local\\Temp\\ipykernel_9568\\3604384760.py:26: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  family_survival = df.groupby('family_size_group')['survived'].agg(['count', 'mean'])\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "按家庭规模分组的生存率:\n",
      "                    人数       生存率\n",
      "family_size_group               \n",
      "独自一人               537  0.303538\n",
      "小家庭                292  0.578767\n",
      "大家庭                 62  0.161290\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 年龄分箱分析\n",
    "# 创建年龄组\n",
    "df['age_group'] = pd.cut(df['age'], bins=[0, 12, 18, 35, 60, 100], \n",
    "                        labels=['儿童', '青少年', '青年', '中年', '老年'])\n",
    "\n",
    "# 按年龄组分析生存率\n",
    "age_group_survival = df.groupby('age_group')['survived'].agg(['count', 'mean'])\n",
    "age_group_survival.columns = ['人数', '生存率']\n",
    "print(\"按年龄组分组的生存率:\")\n",
    "print(age_group_survival)\n",
    "\n",
    "# 可视化年龄组与生存率关系\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.barplot(data=df, x='age_group', y='survived')\n",
    "plt.title('不同年龄组的生存率')\n",
    "plt.ylabel('生存率')\n",
    "plt.xlabel('年龄组')\n",
    "plt.show()\n",
    "\n",
    "# 家庭规模分析\n",
    "df['family_size'] = df['sibsp'] + df['parch'] + 1\n",
    "df['family_size_group'] = pd.cut(df['family_size'], \n",
    "                                bins=[0, 1, 4, 11], \n",
    "                                labels=['独自一人', '小家庭', '大家庭'])\n",
    "\n",
    "family_survival = df.groupby('family_size_group')['survived'].agg(['count', 'mean'])\n",
    "family_survival.columns = ['人数', '生存率']\n",
    "print(\"按家庭规模分组的生存率:\")\n",
    "print(family_survival)\n",
    "\n",
    "# 可视化家庭规模与生存率关系\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.barplot(data=df, x='family_size_group', y='survived')\n",
    "plt.title('不同家庭规模的生存率')\n",
    "plt.ylabel('生存率')\n",
    "plt.xlabel('家庭规模')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "ed88ee53",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==================================================\n",
      "泰坦尼克号乘客生存情况统计分析报告\n",
      "==================================================\n",
      "总乘客数: 891\n",
      "总体生存率: 38.38%\n",
      "\n",
      "性别分析:\n",
      "  female: 314人, 生存率 74.20%\n",
      "  male: 577人, 生存率 18.89%\n",
      "\n",
      "舱位等级分析:\n",
      "  1等舱: 216人, 生存率 62.96%\n",
      "  2等舱: 184人, 生存率 47.28%\n",
      "  3等舱: 491人, 生存率 24.24%\n",
      "\n",
      "年龄分析:\n",
      "  平均年龄: 29.4岁\n",
      "  年龄中位数: 28.0岁\n",
      "  年龄范围: 0.4-80.0岁\n",
      "\n",
      "关键发现:\n",
      "  生存率最高的群体: female 1等舱, 生存率 96.81%\n"
     ]
    }
   ],
   "source": [
    "def generate_statistical_summary(df):\n",
    "    \"\"\"\n",
    "    生成统计分析摘要报告\n",
    "    \"\"\"\n",
    "    print(\"=\"*50)\n",
    "    print(\"泰坦尼克号乘客生存情况统计分析报告\")\n",
    "    print(\"=\"*50)\n",
    "    \n",
    "    # 基本信息\n",
    "    print(f\"总乘客数: {len(df)}\")\n",
    "    print(f\"总体生存率: {df['survived'].mean():.2%}\")\n",
    "    \n",
    "    # 性别分析\n",
    "    print(\"\\n性别分析:\")\n",
    "    gender_stats = df.groupby('sex')['survived'].agg(['count', 'mean'])\n",
    "    for gender in gender_stats.index:\n",
    "        count = gender_stats.loc[gender, 'count']\n",
    "        survival_rate = gender_stats.loc[gender, 'mean']\n",
    "        print(f\"  {gender}: {count}人, 生存率 {survival_rate:.2%}\")\n",
    "    \n",
    "    # 舱位分析\n",
    "    print(\"\\n舱位等级分析:\")\n",
    "    class_stats = df.groupby('pclass')['survived'].agg(['count', 'mean'])\n",
    "    for pclass in sorted(class_stats.index):\n",
    "        count = class_stats.loc[pclass, 'count']\n",
    "        survival_rate = class_stats.loc[pclass, 'mean']\n",
    "        print(f\"  {pclass}等舱: {count}人, 生存率 {survival_rate:.2%}\")\n",
    "    \n",
    "    # 年龄分析\n",
    "    print(\"\\n年龄分析:\")\n",
    "    print(f\"  平均年龄: {df['age'].mean():.1f}岁\")\n",
    "    print(f\"  年龄中位数: {df['age'].median():.1f}岁\")\n",
    "    print(f\"  年龄范围: {df['age'].min():.1f}-{df['age'].max():.1f}岁\")\n",
    "    \n",
    "    print(\"\\n关键发现:\")\n",
    "    # 找出生存率最高的群体\n",
    "    best_survival = df.groupby(['sex', 'pclass'])['survived'].mean().idxmax()\n",
    "    best_rate = df.groupby(['sex', 'pclass'])['survived'].mean().max()\n",
    "    print(f\"  生存率最高的群体: {best_survival[0]} {best_survival[1]}等舱, 生存率 {best_rate:.2%}\")\n",
    "\n",
    "# 生成报告\n",
    "generate_statistical_summary(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7bbcc0e",
   "metadata": {},
   "source": [
    "## 特征工程\n",
    "- 从 Name 中提取称谓信息\n",
    "- 将连续变量（如年龄）分箱处理\n",
    "- 创建家庭规模等新特征"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "06bcb3bd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "原始称谓分布:\n",
      "title\n",
      "Mr          517\n",
      "Miss        182\n",
      "Mrs         125\n",
      "Master       40\n",
      "Dr            7\n",
      "Rev           6\n",
      "Col           2\n",
      "Mlle          2\n",
      "Major         2\n",
      "Ms            1\n",
      "Mme           1\n",
      "Don           1\n",
      "Lady          1\n",
      "Sir           1\n",
      "Capt          1\n",
      "Countess      1\n",
      "Jonkheer      1\n",
      "Name: count, dtype: int64\n",
      "\n",
      "合并后的称谓分布:\n",
      "title\n",
      "Mr        517\n",
      "Miss      185\n",
      "Mrs       126\n",
      "Master     40\n",
      "Rare       23\n",
      "Name: count, dtype: int64\n",
      "\n",
      "不同称谓的生存率:\n",
      "         人数       生存率\n",
      "title                \n",
      "Mrs     126  0.793651\n",
      "Miss    185  0.702703\n",
      "Master   40  0.575000\n",
      "Rare     23  0.347826\n",
      "Mr      517  0.156673\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 从姓名中提取称谓\n",
    "df['title'] = df['name'].str.extract(' ([A-Za-z]+)\\.', expand=False)\n",
    "\n",
    "# 查看称谓分布\n",
    "print(\"原始称谓分布:\")\n",
    "print(df['title'].value_counts())\n",
    "\n",
    "# 合并相似称谓\n",
    "title_mapping = {\n",
    "    'Mr': 'Mr',\n",
    "    'Miss': 'Miss',\n",
    "    'Mrs': 'Mrs',\n",
    "    'Master': 'Master',\n",
    "    'Dr': 'Rare',\n",
    "    'Rev': 'Rare',\n",
    "    'Col': 'Rare',\n",
    "    'Major': 'Rare',\n",
    "    'Mlle': 'Miss',\n",
    "    'Countess': 'Rare',\n",
    "    'Ms': 'Miss',\n",
    "    'Lady': 'Rare',\n",
    "    'Jonkheer': 'Rare',\n",
    "    'Don': 'Rare',\n",
    "    'Dona': 'Rare',\n",
    "    'Mme': 'Mrs',\n",
    "    'Capt': 'Rare',\n",
    "    'Sir': 'Rare'\n",
    "}\n",
    "\n",
    "df['title'] = df['title'].map(title_mapping)\n",
    "df['title'] = df['title'].fillna('Rare')\n",
    "\n",
    "print(\"\\n合并后的称谓分布:\")\n",
    "print(df['title'].value_counts())\n",
    "\n",
    "# 分析不同称谓的生存率\n",
    "title_survival = df.groupby('title')['survived'].agg(['count', 'mean'])\n",
    "title_survival.columns = ['人数', '生存率']\n",
    "title_survival = title_survival.sort_values('生存率', ascending=False)\n",
    "print(\"\\n不同称谓的生存率:\")\n",
    "print(title_survival)\n",
    "\n",
    "# 可视化称谓与生存率关系\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.barplot(data=df, x='title', y='survived', order=title_survival.index)\n",
    "plt.title('不同称谓的生存率')\n",
    "plt.ylabel('生存率')\n",
    "plt.xlabel('称谓')\n",
    "plt.xticks(rotation=45)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "fa1c1a16",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ASUS\\AppData\\Local\\Temp\\ipykernel_9568\\2241867174.py:17: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  age_bin_survival = df.groupby('age_bin')['survived'].agg(['count', 'mean'])\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "按年龄阶段分组的生存率:\n",
      "          人数       生存率\n",
      "age_bin               \n",
      "儿童        69  0.579710\n",
      "青少年       70  0.428571\n",
      "青年       535  0.353271\n",
      "中年       195  0.400000\n",
      "老年        22  0.227273\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 基于业务理解和统计分析进行年龄分箱\n",
    "# 方法1: 按生命周期阶段分箱\n",
    "df['age_bin'] = pd.cut(df['age'], \n",
    "                      bins=[0, 12, 18, 35, 60, 100], \n",
    "                      labels=['儿童', '青少年', '青年', '中年', '老年'])\n",
    "\n",
    "# 方法2: 按四分位数分箱\n",
    "df['age_quartile'] = pd.qcut(df['age'], q=4, labels=['Q1', 'Q2', 'Q3', 'Q4'])\n",
    "\n",
    "# 方法3: 按生存率差异分箱（基于前期分析）\n",
    "# 可以根据年龄与生存率的关系图来确定分箱点\n",
    "df['age_group_simple'] = pd.cut(df['age'], \n",
    "                               bins=[0, 15, 35, 60, 100], \n",
    "                               labels=['Young', 'Adult', 'Middle', 'Senior'])\n",
    "\n",
    "# 分析不同年龄分组的生存率\n",
    "age_bin_survival = df.groupby('age_bin')['survived'].agg(['count', 'mean'])\n",
    "age_bin_survival.columns = ['人数', '生存率']\n",
    "print(\"按年龄阶段分组的生存率:\")\n",
    "print(age_bin_survival)\n",
    "\n",
    "# 可视化年龄分组与生存率关系\n",
    "plt.figure(figsize=(12, 4))\n",
    "\n",
    "plt.subplot(1, 3, 1)\n",
    "sns.barplot(data=df, x='age_bin', y='survived')\n",
    "plt.title('不同年龄阶段的生存率')\n",
    "plt.ylabel('生存率')\n",
    "plt.xticks(rotation=45)\n",
    "\n",
    "plt.subplot(1, 3, 2)\n",
    "sns.barplot(data=df, x='age_quartile', y='survived')\n",
    "plt.title('按四分位数分组的生存率')\n",
    "plt.ylabel('生存率')\n",
    "\n",
    "plt.subplot(1, 3, 3)\n",
    "sns.barplot(data=df, x='age_group_simple', y='survived')\n",
    "plt.title('简单年龄分组的生存率')\n",
    "plt.ylabel('生存率')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "c2ad1ee0",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ASUS\\AppData\\Local\\Temp\\ipykernel_9568\\2310932609.py:13: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  family_survival = df.groupby('family_size_group')['survived'].agg(['count', 'mean'])\n",
      "C:\\Users\\ASUS\\AppData\\Local\\Temp\\ipykernel_9568\\2310932609.py:36: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  family_sex_survival = df.groupby(['family_size_group', 'sex'])['survived'].mean().unstack()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "按家庭规模分组的生存率:\n",
      "                    人数       生存率\n",
      "family_size_group               \n",
      "Alone              537  0.303538\n",
      "Small              292  0.578767\n",
      "Large               62  0.161290\n"
     ]
    },
    {
     "data": {
      "image/png": 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9+VEAILCWuQLqzZo1K+rbBQCgQlNwVBeGvYC4+uXnn3/eXdBWiTNUbAqGqzZ84GLlutABAAgvKkeqZDHPOeec48qRKmO8MKecckoptw756dSwg1WrUtUmLX7Z9mbvtReXvm5DjhpQYEC8dZ1D7et139nG3Zvc7W/Wp1uzmk3dz7FRMXZ70gh7b8XH1r151zJ7HwgPny/dbDt277fberWyqjFRdtM5iXbfmz8XGBCvEh1h917SxgXD5YgmNezrn7a4n9dvzbIxfdtah+Z//e06u2NDW7Q6s4zeDfLkC78M8SLXEL/gggvcwOvEE090mdwKUJ9//vmuRIoyuFW37Mcff7Qnn3zS1QQvCgXPVZv85ZdftipVqljdunXd4pnBK2RrWnfwfd7+/Ch4rhODwA0AgHCii8ce9bPKHFM5seCLzwAAoGJSSdNgTZs2LXKWOEKjZa3mtmzLchcMl5XbV1uzmo0LPGZV5lo7temJFh9byxpUrWcnN0m2hRsz3H3ZOdl225f32ZJNy8qk/Qgvy37fYR1a1HLBcGl7SHVb/kf+CapyTMt4O75Vbffzlh377K1v11v3I+u729ec3tzd71mxcZe1bEDCDsppQPykk05yi10qsKzA+KpVq9xA26vxfeedd7qgtQLnu3fvtvT09AKfb8WKFda3b18XEPdKoSQlJeVajFOP0cBdzxt8n656N2nS5GDeMwAAYUHTpz1an+Paa69163js2LHDLYatTeXOtDg2AABAsMJKrGm8rtnYgVv2vuwya19FpezwDbtyJwPm+HKsepW/6ivn5aPfPrO46Fib2uNJm3jGeHf8p6u/dPft92Xb5j1/Zd8CJW1HVrY1rRuXayZqZGSEbdv1v/r1+Xlt3jo77f55Vr9mjF3Y+ZAD7lcw/ONFf9rFyQfehzLOEPeV8FZZAuKacqVFLLX4pUqkqFyKBtEqW6LA+Oeff24vvPCCe6xKp3z55V9/mPOigLkG5Aqs9+7d2w3MtSnbXB3o5MmT3eOUyda9e3dXR7xXr1721Vdf2ccff2z79u2zBx980Hr06FESnwEAAJW6rqh34jps2DC31odmeR199NFu00D2xhtvDGk7AQBAaK1fv96GDx9uZ5xxhp122mlu69atmzVv3rzA47R2l+IDgdvPb/yVtYz8KaN7X87+XPv25eyz2Kj8Z/GlHHqm7dy3y67+6Ca7+sMbLTIiyq5sf2kZtBbhLioywmKic69VExsdaXv2FR70PP+4Rjaufzv7Zf0um/bl2lz35eT4bNT0ZXZR50OsdUL1Em83isEXfgHxItcQv+aaa/xXiM8++2xX9/vdd9912dtvvvmmu69x48Zu8N2nTx+rWbNmvs/14YcfusxybRMmTMiVET5x4kSXOZ6amuoy2+bMmePuq1+/vo0fP97VRFONzNq1a7MICAAAedi/f78LegfTPl3c1oyv6667zu3LyMhw63kAAICK4dBDD7UffvjBjYsTExPzXVT5119/LfJzXnHFFdagQQM3o0znEUpgu++++9zF9IIEr90l/T4qWgnVcLZj305r/v/rf3viouNsf1CQPNApTU+0l3980/78/zXEX1z6mv3fSXfY5MWvlHp7Ed7iq0Xbz+v/t5i77Mzab1WiCl/QPSY60rq1q2dbztpnL32x1i47+X+VHp756Dfbtnu/paYcWirtBkokIO5R3W6vdnf79u3dv6or7lGWuLbZs2fn+xzKDA/MWgvUsmVLW758uSu5ogU769Wr579PU72VFa5a5comZ/EoAAAOdPHFF7vs70CaieXJzv7fVOYjjzzSf2EbAACUf5pRrcC1lFSS2Lx589xaYT///LPdcccdbl0wjc0VFL/77rvzPU7rkgSvTRJV5a86w8jfz1t/tTNbnOq/3bBafasSWcV27P3f+VqwCItw9cM9dWJrW1REkSf9AwftqGY17Y156/y312zabXv3+yy+WpV8j5n6+RqrW6OKnXtsI3dbwfOAao42e/GfNuXzNTZ9xDH+2uQIoZzyn9Ed8oB4QbTopTpPlUz5OxISEqxnz5553qcr4NoAAEDetJ6HFqxWEPz666932Vv//ve/3X3K+goMjgMAgIrllFNOyfPnv0Prc2km9yWXXOJmcqvMqS6aL1q0qESeH7kt3rTMqkZXtdObdbFPVn9hF7fuZQs3LrYc81n16Gq2e/9u93OgJZuX2YWHnetqjUdHRtsFh/W0b9cvCNl7QPg47tDaro64Fsa84PgE+9cnq+yENrVdKZXtu/db9dgo93OgZvXibNRrP1mDWjHWsFasPT9ntZ1zTEN33/I/dto/Xlpqd13Y2hJqx9nOrGzT4QTGUZZK7HKiaomrHriC2ffee29JPS0AACgmTXueNWuWyx5TibMOHTq40ijKDFfZFGWBBcoJw4wAAADwPw888IBbbFtreqWkpNhxxx3nZmeffPLJoW5apaSg9lPfT7LBR/W3F3s8ZccnHGtTl7zq7nv5nGetRa1mBxwzbembtmzLLzag3aU26MjL7bfta2xCxkshaD3CTXRUhN13cRu7/+2f7YS7vrJPF2+yW3r+Veak86iv7Kd1ucupSLf29e2a05pZ6rQf7bInv7cuh9e1q09t5l9oc9feHBv5yjI77o4v3Xbug9+V+ftCeIvw5Ve7pBiWLVvm6n6r5rcWvVR97/JGHbsW+Ni2bZvVqvW/aUalKiWl4PtnziybdgAAKqS/23fVqVPHtmzZYgsXLrRjjjnGLVKtOqPq+r16owqG63ZgGZWw7rsrCGX5qwSdZ8aMGZSSK0GdUqeGugkoIekP9Q91ExBmKnLftXXrVrcWmM4NNNMsMzPTrrzyymL3L+e9w/93RVU7Nt5axbe0n7Yst8x9zOALhbcj40LdhApj4/a9tnhNph3dopbVqZ5/uRSUrshznyvx5/StGlfizxnRPPf6EpUqQ/zPP/+0f/zjH9apUyc788wz7auvviqXwXAAAMKRpjuL6olv2LDBunbt6rK9NKtLi2RrUwkV73EAAKBi0fpbKlmq/nzBggWuz1epE43Ni0NreD3zzDP2f//3fzZmzBj77bffbPPmzW4hbpSerVnbLH3DQoLhqBBU/uTUdvUIhldGvpyS34pBs5mTkpJcQldqamq+607mdzH3kEMOsZUrV5Z8DXF1hq+++qrVr1/fXS1Wx6gO87PPPrOLLrrI5s+fb4cffnixXhgAAJQe9df9+vXz31Yf/t5771m3bt3s6aefthtuuMF/n7dYNgAAqFgGDBhg7dq1c7PAbr75ZlfuRIGEYcOGuRliRaUL5ppN1rRp01JtLwAAgbKyslzfpX5o+vTpNmLECLdg9MCBA60oFEBfv369FVeRAuKrV6+21157zdUdVVkULZ6pIHmjRo3cdDAG0gAAlC/qr59//vlc++Li4iitAQBAJaKscCWvabbX999/b5988on98ccf9uijjxbreY4//ngbOnSoC0oAAMJMTujWlNLaVyozphlJWgMrLS3NrrvuuiIFxD///HN75513rF69eqUTENdCGsoCD6QOV9OwXnjhBXclWVeg7777bqtatWqxGwEAAMpGw4Z/re7u2bdvn51zzjn20UcfhaxNAADg4LRs2dIFxFUGTdPNdUF89uzZ1qJFi2I9j7LMtTZFQkKCu4Ae6Ndffy3hVgMA8BfNZkpOTnbBcOnQoYMtWbLEipJZPmTIEHv88cfttttus1IJiOdFge/u3bu7TYtqDho0yE455RT74IMPXM0XAAAQeqqp1qVLF1u0aJG/lIoGyx7N/po7d24IWwgAAA7WY489ZldccYULJLz00ksuW+7qq6+2adOmFet5Jk6caM8995y1bt261NoKACincopes7uoFLDWFig2NtZtwYtRJyYm+m9HRES4MmBbtmwpML6sTPI2bdpYnz59Diog/rcW1fS0bdvW5syZY82bN3eDbq1GDQAAQq9KlSr2yy+/+LPBdVtZZIEnHNoHAAAqHiWorVu3zi2uecIJJ7gscS2kfcEFFxTrefr27evG9Ape6NwgcAMAhEHJlJyS3bRAs8psB27aF0wJWsFBcs1U2rVrV77NXbp0qT377LNuMeiDddAZ4sEUvddUrdNOO82lrL/88ssl9dQAAOAg6QTDC3h7/wav/cFgFwCAyiG4hKnKn77xxhuuFEpB3n//fffv119/batWrbLatWu7gITOGSiZAgAorpEjR7rFngMFB76lbt26lpGRkWufEq3zW69SC0cPHjzY7r//fmvcuLEdrBLJEA8Mimua1bfffmubN28uyacGAAAl1FcDAIDwsHjx4lwzwwIps/yiiy6y3r1724oVK9ym2wo2KBihC+n//ve/y7zNAICKLzY21mrVqpVryysgrplNgSU81RdptpIC5XnRRdsvv/zSUlNT3cVbbdqn2uPFSc4usQzxwPIpqlPK4poAAIT2irz64v3797uB8L333uuvIe79DAAAwteAAQOsRo0ads8997jbqkH+1FNPuUU5VQpV+0eMGOFKqQAAKrGcnJC9dNeuXV0d8cmTJ9vAgQNdbXCVA1Mil9bDqlmzZq6kriZNmrigefBsqOnTp1vHjh1DFxAXguEAAISWaonqCrwC4Np+/vln/32BPwMAgPA0b948V4dVwYXs7Gy766673MJkCk7Itddea48++miomwkAqOQlPidOnOjWslDWd2RkpP9CrBbVXLBgQa5Atx7fsmXLA56jadOm7iJvkV+3BN8DAAAoJ1577TX37549e2zmzJn24osvutta78P72ZPfdLSSoilsWuBL09jyqwUHAADKVps2beyTTz6x/v3729ixY10m3k033eS//4svvrBDDjkkpG0EAJSBHF9IX75Xr14uoSs9Pd2Sk5OtXr16br9KeBXFypUrQ1tDHAAAlC8luWCmFjtRjTddqdfV+6KcoGghlWOPPdb69etniYmJ9uOPP5ZYewAAwMGfBzz55JN26623uhnemqI+adIkNzVdi2iedtppduWVV9r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      "text/plain": [
       "<Figure size 1500x500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 创建家庭规模特征\n",
    "df['family_size'] = df['sibsp'] + df['parch'] + 1  # +1 包括自己\n",
    "\n",
    "# 创建是否独自旅行的特征\n",
    "df['is_alone'] = (df['family_size'] == 1).astype(int)\n",
    "\n",
    "# 创建家庭规模分组特征\n",
    "df['family_size_group'] = pd.cut(df['family_size'], \n",
    "                                bins=[0, 1, 4, 11], \n",
    "                                labels=['Alone', 'Small', 'Large'])\n",
    "\n",
    "# 分析家庭规模与生存率关系\n",
    "family_survival = df.groupby('family_size_group')['survived'].agg(['count', 'mean'])\n",
    "family_survival.columns = ['人数', '生存率']\n",
    "print(\"按家庭规模分组的生存率:\")\n",
    "print(family_survival)\n",
    "\n",
    "# 可视化家庭规模与生存率关系\n",
    "plt.figure(figsize=(15, 5))\n",
    "\n",
    "plt.subplot(1, 3, 1)\n",
    "plt.hist([df[df['survived']==1]['family_size'], df[df['survived']==0]['family_size']], \n",
    "         bins=20, alpha=0.7, label=['Survived', 'Not Survived'], color=['green', 'red'])\n",
    "plt.xlabel('家庭规模')\n",
    "plt.ylabel('人数')\n",
    "plt.title('家庭规模分布')\n",
    "plt.legend()\n",
    "\n",
    "plt.subplot(1, 3, 2)\n",
    "sns.barplot(data=df, x='family_size_group', y='survived')\n",
    "plt.title('不同家庭规模的生存率')\n",
    "plt.ylabel('生存率')\n",
    "\n",
    "plt.subplot(1, 3, 3)\n",
    "# 家庭规模与性别、生存率的交叉分析\n",
    "family_sex_survival = df.groupby(['family_size_group', 'sex'])['survived'].mean().unstack()\n",
    "sns.heatmap(family_sex_survival, annot=True, cmap='RdYlGn', center=0.5)\n",
    "plt.title('家庭规模、性别与生存率关系热力图')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "9d1220d0",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ASUS\\AppData\\Local\\Temp\\ipykernel_9568\\2790521676.py:8: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  fare_survival = df.groupby('fare_bin')['survived'].agg(['count', 'mean'])\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "按票价分组的生存率:\n",
      "            人数       生存率\n",
      "fare_bin                \n",
      "Low        223  0.197309\n",
      "Medium     224  0.303571\n",
      "High       222  0.454955\n",
      "Very High  222  0.581081\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 票价分箱处理\n",
    "df['fare_bin'] = pd.qcut(df['fare'], q=4, labels=['Low', 'Medium', 'High', 'Very High'])\n",
    "\n",
    "# 创建是否免费票的特征\n",
    "df['is_free_ticket'] = (df['fare'] == 0).astype(int)\n",
    "\n",
    "# 分析票价分组与生存率关系\n",
    "fare_survival = df.groupby('fare_bin')['survived'].agg(['count', 'mean'])\n",
    "fare_survival.columns = ['人数', '生存率']\n",
    "print(\"按票价分组的生存率:\")\n",
    "print(fare_survival)\n",
    "\n",
    "# 可视化票价与生存率关系\n",
    "plt.figure(figsize=(12, 4))\n",
    "\n",
    "plt.subplot(1, 2, 1)\n",
    "sns.boxplot(data=df, x='survived', y='fare')\n",
    "plt.title('生存状态与票价分布')\n",
    "plt.xlabel('生存状态')\n",
    "plt.ylabel('票价')\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "sns.barplot(data=df, x='fare_bin', y='survived')\n",
    "plt.title('不同票价区间的生存率')\n",
    "plt.ylabel('生存率')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "441974b2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "按舱位字母分组的生存率:\n",
      "               人数       生存率\n",
      "cabin_letter               \n",
      "D              33  0.757576\n",
      "E              32  0.750000\n",
      "B              47  0.744681\n",
      "F              13  0.615385\n",
      "C              59  0.593220\n",
      "G               4  0.500000\n",
      "A              15  0.466667\n",
      "Unknown       687  0.299854\n",
      "T               1  0.000000\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 从舱位号中提取更多信息\n",
    "df['cabin_letter'] = df['cabin'].str.extract('([A-Z])')\n",
    "df['cabin_letter'] = df['cabin_letter'].fillna('Unknown')\n",
    "\n",
    "# 分析舱位字母与生存率关系\n",
    "cabin_survival = df.groupby('cabin_letter')['survived'].agg(['count', 'mean'])\n",
    "cabin_survival.columns = ['人数', '生存率']\n",
    "cabin_survival = cabin_survival.sort_values('生存率', ascending=False)\n",
    "print(\"按舱位字母分组的生存率:\")\n",
    "print(cabin_survival)\n",
    "\n",
    "# 可视化舱位字母与生存率关系\n",
    "plt.figure(figsize=(12, 5))\n",
    "\n",
    "plt.subplot(1, 2, 1)\n",
    "cabin_counts = df['cabin_letter'].value_counts()\n",
    "plt.bar(range(len(cabin_counts)), cabin_counts.values)\n",
    "plt.xlabel('舱位字母')\n",
    "plt.ylabel('人数')\n",
    "plt.title('各舱位字母的人数分布')\n",
    "plt.xticks(range(len(cabin_counts)), cabin_counts.index)\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "sns.barplot(data=df, x='cabin_letter', y='survived', \n",
    "            order=cabin_survival.index)\n",
    "plt.title('不同舱位字母的生存率')\n",
    "plt.ylabel('生存率')\n",
    "plt.xticks(rotation=45)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "d26fb9a3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "性别与舱位等级组合的生存率:\n",
      "             人数       生存率\n",
      "sex_pclass               \n",
      "female_1     94  0.968085\n",
      "female_2     76  0.921053\n",
      "female_3    144  0.500000\n",
      "male_1      122  0.368852\n",
      "male_2      108  0.157407\n",
      "male_3      347  0.135447\n",
      "\n",
      "家庭规模与舱位等级组合的生存率:\n",
      "               人数       生存率\n",
      "family_class               \n",
      "Large_2         2  1.000000\n",
      "Small_1       101  0.732673\n",
      "Large_1         6  0.666667\n",
      "Small_2        78  0.628205\n",
      "Alone_1       109  0.532110\n",
      "Small_3       113  0.407080\n",
      "Alone_2       104  0.346154\n",
      "Alone_3       324  0.212963\n",
      "Large_3        54  0.074074\n"
     ]
    }
   ],
   "source": [
    "# 创建更有意义的组合特征\n",
    "# 1. 性别与舱位等级的组合\n",
    "df['sex_pclass'] = df['sex'].astype(str) + '_' + df['pclass'].astype(str)\n",
    "\n",
    "# 2. 年龄组与性别组合\n",
    "df['age_sex'] = df['age_group_simple'].astype(str) + '_' + df['sex'].astype(str)\n",
    "\n",
    "# 3. 家庭规模与舱位等级组合\n",
    "df['family_class'] = df['family_size_group'].astype(str) + '_' + df['pclass'].astype(str)\n",
    "\n",
    "# 4. 是否有 cabin 信息\n",
    "df['has_cabin'] = (df['cabin'] != 'unknown').astype(int)\n",
    "\n",
    "# 5. 票号前缀特征（可能反映登船港口等信息）\n",
    "df['ticket_prefix'] = df['ticket'].str.extract('([A-Za-z]+)', expand=False)\n",
    "df['ticket_prefix'] = df['ticket_prefix'].fillna('None')\n",
    "\n",
    "# 分析新特征的有效性\n",
    "print(\"性别与舱位等级组合的生存率:\")\n",
    "sex_pclass_survival = df.groupby('sex_pclass')['survived'].agg(['count', 'mean'])\n",
    "sex_pclass_survival.columns = ['人数', '生存率']\n",
    "sex_pclass_survival = sex_pclass_survival.sort_values('生存率', ascending=False)\n",
    "print(sex_pclass_survival)\n",
    "\n",
    "print(\"\\n家庭规模与舱位等级组合的生存率:\")\n",
    "family_class_survival = df.groupby('family_class')['survived'].agg(['count', 'mean'])\n",
    "family_class_survival.columns = ['人数', '生存率']\n",
    "family_class_survival = family_class_survival.sort_values('生存率', ascending=False)\n",
    "print(family_class_survival)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "141610c2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "特征与生存率的相关性:\n",
      "survived             1.000000\n",
      "sex                 -0.543351\n",
      "pclass              -0.338481\n",
      "has_cabin            0.316912\n",
      "cabin_letter        -0.301116\n",
      "fare                 0.257307\n",
      "family_size_group    0.249714\n",
      "is_alone            -0.203367\n",
      "embarked            -0.167675\n",
      "fare_bin             0.111219\n",
      "parch                0.081629\n",
      "title               -0.071174\n",
      "age_bin             -0.067534\n",
      "age                 -0.064910\n",
      "sibsp               -0.035322\n",
      "family_size          0.016639\n",
      "Name: survived, dtype: float64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "e:\\Documents\\git-notebook\\.venv\\lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 8722 (\\N{MINUS SIGN}) missing from font(s) SimHei.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 准备用于初步特征分析的数据\n",
    "# 选择分类特征进行编码\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "# 创建用于分析的特征子集\n",
    "feature_columns = ['pclass', 'sex', 'age', 'sibsp', 'parch', 'fare', \n",
    "                  'embarked', 'title', 'age_bin', 'family_size', 'is_alone',\n",
    "                  'family_size_group', 'fare_bin', 'cabin_letter', 'has_cabin']\n",
    "\n",
    "# 对分类变量进行标签编码\n",
    "df_encoded = df[feature_columns + ['survived']].copy()\n",
    "\n",
    "# 对分类特征进行编码\n",
    "label_encoders = {}\n",
    "for col in ['pclass', 'sex', 'embarked', 'title', 'age_bin', 'family_size_group', \n",
    "           'fare_bin', 'cabin_letter']:\n",
    "    if col in df_encoded.columns:\n",
    "        le = LabelEncoder()\n",
    "        df_encoded[col] = le.fit_transform(df_encoded[col].astype(str))\n",
    "        label_encoders[col] = le\n",
    "\n",
    "# 计算特征与目标变量的相关性\n",
    "correlation_with_survival = df_encoded.corr()['survived'].sort_values(key=abs, ascending=False)\n",
    "print(\"特征与生存率的相关性:\")\n",
    "print(correlation_with_survival)\n",
    "\n",
    "# 可视化特征重要性\n",
    "plt.figure(figsize=(10, 8))\n",
    "correlation_features = correlation_with_survival.drop('survived')\n",
    "sns.barplot(x=correlation_features.values, y=correlation_features.index)\n",
    "plt.title('特征与生存率的相关性')\n",
    "plt.xlabel('相关系数')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "cb501fa5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "============================================================\n",
      "泰坦尼克号数据特征工程总结报告\n",
      "============================================================\n",
      "新增特征及其取值:\n",
      "  title: 5 个唯一值\n",
      "    取值: ['Mr', 'Mrs', 'Miss', 'Master', 'Rare']\n",
      "  age_bin: 5 个唯一值\n",
      "    取值: ['青年', '中年', '儿童', '青少年', '老年']\n",
      "  family_size: 9 个唯一值\n",
      "    取值: [np.int64(2), np.int64(1), np.int64(5), np.int64(3), np.int64(7), np.int64(6), np.int64(4), np.int64(8), np.int64(11)]\n",
      "  is_alone: 2 个唯一值\n",
      "    取值: [np.int64(0), np.int64(1)]\n",
      "  family_size_group: 3 个唯一值\n",
      "    取值: ['Small', 'Alone', 'Large']\n",
      "  fare_bin: 4 个唯一值\n",
      "    取值: ['Low', 'Very High', 'Medium', 'High']\n",
      "  cabin_letter: 9 个唯一值\n",
      "    取值: ['Unknown', 'C', 'E', 'G', 'D', 'A', 'B', 'F', 'T']\n",
      "  has_cabin: 2 个唯一值\n",
      "    取值: [np.int64(0), np.int64(1)]\n",
      "\n",
      "发现的重要特征组合:\n",
      "  1. 女性1等舱乘客生存率最高 (96.81%)\n",
      "  2. 独自旅行的男性生存率最低 (15.15%)\n",
      "  3. 儿童和青少年生存率相对较高\n",
      "  4. 高票价乘客生存率明显更高\n",
      "  5. 有舱位信息的乘客生存率更高\n",
      "\n",
      "特征工程建议:\n",
      "  1. 可以进一步探索票号前缀与生存率的关系\n",
      "  2. 考虑创建交互特征，如年龄×性别、票价×舱位等\n",
      "  3. 可以尝试更精细的分箱策略\n",
      "  4. 考虑使用机器学习方法进行特征选择\n"
     ]
    }
   ],
   "source": [
    "def feature_engineering_summary(df):\n",
    "    \"\"\"\n",
    "    生成特征工程总结报告\n",
    "    \"\"\"\n",
    "    print(\"=\"*60)\n",
    "    print(\"泰坦尼克号数据特征工程总结报告\")\n",
    "    print(\"=\"*60)\n",
    "    \n",
    "    # 新增特征统计\n",
    "    new_features = ['title', 'age_bin', 'family_size', 'is_alone', \n",
    "                   'family_size_group', 'fare_bin', 'cabin_letter', 'has_cabin']\n",
    "    \n",
    "    print(\"新增特征及其取值:\")\n",
    "    for feature in new_features:\n",
    "        if feature in df.columns:\n",
    "            unique_values = df[feature].nunique()\n",
    "            print(f\"  {feature}: {unique_values} 个唯一值\")\n",
    "            if unique_values <= 10:  # 只显示类别较少的特征的具体值\n",
    "                print(f\"    取值: {list(df[feature].unique())}\")\n",
    "    \n",
    "    # 最有预测力的特征组合\n",
    "    print(\"\\n发现的重要特征组合:\")\n",
    "    print(\"  1. 女性1等舱乘客生存率最高 (96.81%)\")\n",
    "    print(\"  2. 独自旅行的男性生存率最低 (15.15%)\")\n",
    "    print(\"  3. 儿童和青少年生存率相对较高\")\n",
    "    print(\"  4. 高票价乘客生存率明显更高\")\n",
    "    print(\"  5. 有舱位信息的乘客生存率更高\")\n",
    "    \n",
    "    # 特征工程建议\n",
    "    print(\"\\n特征工程建议:\")\n",
    "    print(\"  1. 可以进一步探索票号前缀与生存率的关系\")\n",
    "    print(\"  2. 考虑创建交互特征，如年龄×性别、票价×舱位等\")\n",
    "    print(\"  3. 可以尝试更精细的分箱策略\")\n",
    "    print(\"  4. 考虑使用机器学习方法进行特征选择\")\n",
    "\n",
    "# 生成特征工程总结\n",
    "feature_engineering_summary(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0692d61e",
   "metadata": {},
   "source": [
    "### 相关性分析\n",
    "需要强调上面我们对数据集的分类特征采取了标签编码，然后用皮尔逊相关系数，即两个变量之间的线性关系，来计算相关性。\n",
    "**这种方式的分析是有可能误导我们的判断的，因为LabelEncoder给类别分配数字(0,1,2,3)，这暗示了顺序关系，但实际上顺序关系并不一定存在。**\n",
    "我们应该采取其他方式来评估变量之间的相关性。\n",
    "| 方法           | 用途             | 适用场景           | 解释               |\n",
    "| :------------- | :--------------- | :----------------- | :----------------- |\n",
    "| **皮尔逊相关** | 连续变量线性关系 | 数值型变量         | 对分类变量无意义   |\n",
    "| **卡方检验**   | 变量独立性检验   | 分类变量           | p<0.05表示相关     |\n",
    "| **Cramér's V** | 关联强度         | 分类变量           | 0-1，越接近1越相关 |\n",
    "| **信息值(IV)** | 预测能力         | 分类变量vs二元目标 | >0.1有预测价值     |\n",
    "\n",
    "#### A. 卡方检验 (Chi-Square Test)\n",
    "用于检验两个分类变量是否独立：\n",
    "```python\n",
    "from scipy.stats import chi2_contingency\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "def chi_square_test(df, col1, col2):\n",
    "    \"\"\"\n",
    "    卡方检验：检验两个分类变量是否独立\n",
    "    \"\"\"\n",
    "    # 创建列联表\n",
    "    contingency_table = pd.crosstab(df[col1], df[col2])\n",
    "    print(f\"{col1} vs {col2} 列联表:\")\n",
    "    print(contingency_table)\n",
    "    \n",
    "    # 执行卡方检验\n",
    "    chi2, p_value, dof, expected = chi2_contingency(contingency_table)\n",
    "    \n",
    "    print(f\"\\n卡方统计量: {chi2:.4f}\")\n",
    "    print(f\"自由度: {dof}\")\n",
    "    print(f\"p值: {p_value:.4f}\")\n",
    "    \n",
    "    if p_value < 0.05:\n",
    "        print(\"结论: 两个变量相关(拒绝独立假设)\")\n",
    "    else:\n",
    "        print(\"结论: 两个变量独立(接受独立假设)\")\n",
    "    \n",
    "    return chi2, p_value\n",
    "\n",
    "# 示例应用\n",
    "# chi_square_test(df, 'sex', 'survived')\n",
    "```\n",
    "#### B. Cramér's V\n",
    "衡量分类变量间关联强度(0-1之间，越接近1关联越强)：\n",
    "```python\n",
    "def cramers_v(x, y):\n",
    "    \"\"\"\n",
    "    计算Cramér's V统计量，衡量两个分类变量的关联强度\n",
    "    \"\"\"\n",
    "    confusion_matrix = pd.crosstab(x, y)\n",
    "    chi2 = chi2_contingency(confusion_matrix)[0]\n",
    "    n = confusion_matrix.sum().sum()\n",
    "    phi2 = chi2/n\n",
    "    r, k = confusion_matrix.shape\n",
    "    \n",
    "    # 偏倚校正\n",
    "    phi2corr = max(0, phi2 - ((k-1)*(r-1))/(n-1))\n",
    "    rcorr = r - ((r-1)**2)/(n-1)\n",
    "    kcorr = k - ((k-1)**2)/(n-1)\n",
    "    \n",
    "    return np.sqrt(phi2corr/min((kcorr-1),(rcorr-1)))\n",
    "\n",
    "# 示例\n",
    "# cramers_v_value = cramers_v(df['sex'], df['survived'])\n",
    "# print(f\"Sex与Survived的Cramér's V: {cramers_v_value:.4f}\")\n",
    "```\n",
    "#### C. 信息值 (Information Value)\n",
    "衡量分类变量对二元目标变量的预测能力：\n",
    "```python\n",
    "def calculate_woe_iv(df, feature, target):\n",
    "    \"\"\"\n",
    "    计算Weight of Evidence和Information Value\n",
    "    \"\"\"\n",
    "    lst = []\n",
    "    for i in range(df[feature].nunique()):\n",
    "        val = list(df[feature].unique())[i]\n",
    "        lst.append([\n",
    "            feature, val,\n",
    "            df[df[feature] == val].count().iloc[0],\n",
    "            df[(df[feature] == val) & (df[target] == 0)].count().iloc[0],\n",
    "            df[(df[feature] == val) & (df[target] == 1)].count().iloc[0]\n",
    "        ])\n",
    "    \n",
    "    data = pd.DataFrame(lst, columns=['Variable', 'Value', 'All', 'Good', 'Bad'])\n",
    "    data['Share'] = data['All'] / data['All'].sum()\n",
    "    data['Bad Rate'] = data['Bad'] / data['All']\n",
    "    data['Distribution Good'] = data['Good'] / data['Good'].sum()\n",
    "    data['Distribution Bad'] = data['Bad'] / data['Bad'].sum()\n",
    "    \n",
    "    # 避免除零错误\n",
    "    data['Distribution Good'] = data['Distribution Good'].replace(0, 1e-10)\n",
    "    data['Distribution Bad'] = data['Distribution Bad'].replace(0, 1e-10)\n",
    "    \n",
    "    data['WoE'] = np.log(data['Distribution Good'] / data['Distribution Bad'])\n",
    "    data['IV'] = (data['Distribution Good'] - data['Distribution Bad']) * data['WoE']\n",
    "    \n",
    "    iv = data['IV'].sum()\n",
    "    return data, iv\n",
    "\n",
    "# IV解释标准:\n",
    "# < 0.02: 无预测能力\n",
    "# 0.02-0.1: 较弱的预测能力\n",
    "# 0.1-0.3: 中等预测能力\n",
    "# 0.3-0.5: 强预测能力\n",
    "# > 0.5: 很可能过拟合\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a9e06d8b",
   "metadata": {},
   "outputs": [],
   "source": [
    "## todo: 实际执行验证卡方检验等方法统计\n",
    "# 泰坦尼克数据的实际分析\n",
    "def analyze_categorical_associations(df):\n",
    "    \"\"\"\n",
    "    分析泰坦尼克数据中分类变量的关联性\n",
    "    \"\"\"\n",
    "    categorical_features = ['sex', 'pclass', 'embarked', 'title']\n",
    "    \n",
    "    print(\"=== 分类变量关联性分析 ===\\n\")\n",
    "    \n",
    "    # 1. 各特征与生存率的卡方检验和Cramér's V\n",
    "    for feature in categorical_features:\n",
    "        print(f\"--- {feature} vs Survived ---\")\n",
    "        \n",
    "        # 卡方检验\n",
    "        contingency_table = pd.crosstab(df[feature], df['survived'])\n",
    "        chi2, p_value = chi_square_test(contingency_table)\n",
    "        \n",
    "        print(f\"卡方统计量: {chi2:.4f}\")\n",
    "        print(f\"p值: {p_value:.6f}\")\n",
    "        \n",
    "        if p_value < 0.05:\n",
    "            print(\"结果: 显著相关\")\n",
    "        else:\n",
    "            print(\"结果: 无显著相关\")\n",
    "        print()\n",
    "    \n",
    "    # 2. 特征间关联性分析\n",
    "    print(\"=== 特征间关联性分析 ===\")\n",
    "    for i in range(len(categorical_features)):\n",
    "        for j in range(i+1, len(categorical_features)):\n",
    "            feature1 = categorical_features[i]\n",
    "            feature2 = categorical_features[j]\n",
    "            \n",
    "            cramers_v_value = cramers_v(df[feature1], df[feature2])\n",
    "            print(f\"{feature1} vs {feature2}: Cramér's V = {cramers_v_value:.4f}\")\n",
    "\n",
    "# 信息值分析(针对生存预测)\n",
    "def analyze_predictive_power(df):\n",
    "    \"\"\"\n",
    "    分析各分类特征对生存的预测能力\n",
    "    \"\"\"\n",
    "    categorical_features = ['sex', 'pclass', 'embarked', 'title']\n",
    "    \n",
    "    print(\"=== 特征预测能力分析(信息值) ===\")\n",
    "    for feature in categorical_features:\n",
    "        try:\n",
    "            _, iv = calculate_woe_iv(df, feature, 'survived')\n",
    "            print(f\"{feature}: IV = {iv:.4f}\", end=\" \")\n",
    "            \n",
    "            if iv < 0.02:\n",
    "                print(\"(无预测能力)\")\n",
    "            elif iv < 0.1:\n",
    "                print(\"(较弱预测能力)\")\n",
    "            elif iv < 0.3:\n",
    "                print(\"(中等预测能力)\")\n",
    "            elif iv < 0.5:\n",
    "                print(\"(强预测能力)\")\n",
    "            else:\n",
    "                print(\"(很可能过拟合)\")\n",
    "        except Exception as e:\n",
    "            print(f\"{feature}: 计算出错 - {e}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c36f09f",
   "metadata": {},
   "source": [
    "# 进阶部分\n",
    "\n",
    "## 机器学习模型训练\n",
    "- 逻辑回归、随机森林、支持向量机等算法对比\n",
    "- 交叉验证评估模型性能\n",
    "- 特征重要性分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "15bc18b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 数据预处理和特征工程\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler, LabelEncoder\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# 假设您已经完成了特征工程，现在准备建模数据\n",
    "# 选择用于建模的特征\n",
    "features = ['pclass', 'sex', 'age', 'sibsp', 'parch', 'fare', \n",
    "           'embarked', 'title', 'age_bin', 'family_size_group', \n",
    "           'is_alone', 'has_cabin']\n",
    "\n",
    "# 处理分类变量\n",
    "df_model = df[features + ['survived']].copy()\n",
    "\n",
    "# 对分类特征进行独热编码 (One-Hot Encoding)\n",
    "categorical_features = ['pclass', 'sex', 'embarked', 'title', 'age_bin', 'family_size_group']\n",
    "df_encoded = pd.get_dummies(df_model, columns=categorical_features, drop_first=True)\n",
    "\n",
    "# 分离特征和目标变量\n",
    "X = df_encoded.drop('survived', axis=1)\n",
    "y = df_encoded['survived']\n",
    "\n",
    "# 划分训练集和测试集\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d5531a0a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 模型训练与评估\n",
    "# 导入常用机器学习模型\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix\n",
    "\n",
    "# 1. 逻辑回归\n",
    "log_reg = LogisticRegression(random_state=42)\n",
    "log_reg.fit(X_train, y_train)\n",
    "y_pred_lr = log_reg.predict(X_test)\n",
    "print(\"逻辑回归准确率:\", accuracy_score(y_test, y_pred_lr))\n",
    "\n",
    "# 2. 随机森林\n",
    "rf = RandomForestClassifier(n_estimators=100, random_state=42)\n",
    "rf.fit(X_train, y_train)\n",
    "y_pred_rf = rf.predict(X_test)\n",
    "print(\"随机森林准确率:\", accuracy_score(y_test, y_pred_rf))\n",
    "\n",
    "# 3. 支持向量机\n",
    "svm = SVC(random_state=42)\n",
    "svm.fit(X_train, y_train)\n",
    "y_pred_svm = svm.predict(X_test)\n",
    "print(\"SVM准确率:\", accuracy_score(y_test, y_pred_svm))\n",
    "\n",
    "# 4. K近邻\n",
    "knn = KNeighborsClassifier()\n",
    "knn.fit(X_train, y_train)\n",
    "y_pred_knn = knn.predict(X_test)\n",
    "print(\"KNN准确率:\", accuracy_score(y_test, y_pred_knn))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "686dc505",
   "metadata": {},
   "source": [
    "## 模型优化\n",
    "- 超参数调优\n",
    "- 特征选择\n",
    "- 集成学习方法"
   ]
  }
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